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# -*- coding: utf-8 -*-
# Generated by Django 1.11.3 on 2017-08-29 07:54
from __future__ import unicode_literals
from django.db import migrations, models
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import io
import logging
import mock
import mocks
import monitor
import re
import time
import unittest
if __name__ == '__main__':
unittest.main()
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import os
import numpy as np
from scipy.interpolate import InterpolatedUnivariateSpline as ius
from scipy import integrate
import george
from george import kernels
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import geopandas as gpd
import rasterio as rio
from shapely.geometry import box
import matplotlib.pyplot as plt
from descartes import PolygonPatch
from application_logger import logghandler
logger=logghandler('test','Geo_log')
user_logger= logger.get_logger("DEBUG")
if __name__=="__main__":
plotter_obj= Plotter("IA_FullState.tif","IA_FullState.shp")
#plotter_obj.get_shp_from_tif()
plotter_obj.plot_geometry()
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import pandas as pd
from finnhub_python.utils import RequestCache
class FinnHubOptionChain(RequestCache):
"""
Wrapper class for option chain data returned
by FinnHubs api.
"""
@property
@property
@property
@property
def get_calls(self, expiry):
"""
Get a dataframe of calls for an expiration.
Dataframe is indexed by strike
:param expiry: str, date
:return: pandas.DataFrame
"""
opts = self._get_side(expiry, 'CALL')
df = pd.DataFrame(opts)
df.index = df.strike
return df
def get_puts(self, expiry):
"""
Get a dataframe of puts for an expiration.
Dataframe is indexed by strike
:param expiry: str, date
:return: pandas.DataFrame
"""
opts = self._get_side(expiry, 'PUT')
df = pd.DataFrame(opts)
df.index = df.strike
return df
def get_option(self, expiry, side, strike):
""" Get a single option row """
if side.upper() == 'CALL':
opts = self.get_calls(expiry)
elif side.upper() == 'PUT':
opts = self.get_puts(expiry)
else:
raise ValueError('Invalid Option Side: {}'.format(side))
return opts.loc[strike]
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# Stub models file
from dimagi.ext.couchdbkit import Document
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from django.utils.translation import ugettext_lazy as _
from django import forms
from protocols.models import Step,Protocol,OperateStep,ThermocycleStep,Input,Output
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import requests
import re
url="http://zyk.bjhd.gov.cn/zwdt/hdyw/"
headers={
'Referer':'http://zyk.bjhd.gov.cn/zwdt/hdyw/',
'User-Agent':'Mozilla/5.0 (Windows NT 10.0; WOW64; Trident/7.0; Touch; LCTE; rv:11.0) like Gecko'
}
response=requests.get(url)
response.encoding='utf-8'
# r=respose.text
HTML=response.text
# compile预编译 适用于文本过长
regex=re.compile('.*?if\(!strLink\){document\.write\(\'<a href="\.(.*shtml)".*\'\)}')
result=regex.findall(HTML)
print(result)
url = ['http://zyk.bjhd.gov.cn/zwdt/hdyw' + i for i in result]
print(url)
print()
for j in url:
print(j)
# with open('/home/gz/Desktop/zy3.txt',mode='w',encoding='utf8')as f:
# f.write(r)
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import tensorflow as tf
import numpy as np
from tensorflow.keras import Model, Sequential
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.layers import (Dense, Conv1D, Embedding, UpSampling1D, AveragePooling1D,
AveragePooling2D, GlobalAveragePooling2D, Activation, LayerNormalization, Dropout, Layer)
import sys
import tensorflow_probability as tfp
tfd = tfp.distributions
from torchvision.transforms import ToTensor
import torch
#takes a grayscale image (with the last channel) with pixels [0, 255]
#rescales to [-1, 1] and repeats along the channel axis for 3 channels
#uses a MobileNetV2 with pretrained weights from imagenet as initial weights | [
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257,
12173,
7934,
53,
17,
351,
2181,
13363,
19590,
422,
3590,
268,
316,
355,
4238,
19590
] | 3.184332 | 217 |
# coding: utf-8
#
import pytest
import numpy as np
import os
from sympde.topology import Domain, Line, Square, Cube
from psydac.cad.geometry import Geometry, export_nurbs_to_hdf5, refine_nurbs
from psydac.cad.geometry import import_geopdes_to_nurbs
from psydac.cad.cad import elevate, refine
from psydac.cad.gallery import quart_circle, circle
from psydac.mapping.discrete import SplineMapping, NurbsMapping
from psydac.mapping.discrete_gallery import discrete_mapping
from psydac.fem.splines import SplineSpace
from psydac.fem.tensor import TensorFemSpace
from psydac.utilities.utils import refine_array_1d
base_dir = os.path.dirname(os.path.realpath(__file__))
#==============================================================================
#==============================================================================
#==============================================================================
# TODO to be removed
#==============================================================================
# TODO to be removed
#==============================================================================
@pytest.mark.parametrize( 'ncells', [[8,8], [12,12], [14,14]] )
@pytest.mark.parametrize( 'degree', [[2,2], [3,2], [2,3], [3,3], [4,4]] )
@pytest.mark.parametrize( 'ncells', [[8,8], [12,12], [14,14]] )
@pytest.mark.parametrize( 'degree', [[2,2], [3,2], [2,3], [3,3], [4,4]] )
#==============================================================================
@pytest.mark.xfail
#==============================================================================
# CLEAN UP SYMPY NAMESPACE
#==============================================================================
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] | 3.136042 | 566 |
import numpy as np
import matplotlib.pyplot as plt
from sklearn import preprocessing
from skimage.feature import local_binary_pattern
#from skimage import io
from sklearn.cross_validation import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
import glob
radius = 3
n_points = 8 * radius
X = []
y = []
cls_fldrs = sorted(glob.glob('GTSRB_subset/*'))
for i , folder in enumerate(cls_fldrs): # i = 0 or 1 folder = ['GTSRB_subset\\class1', 'GTSRB_subset\\class2']
names = glob.glob(folder + '/*jpg')
for name in names:
img = plt.imread(name)
lbp = local_binary_pattern(img, n_points, radius)
histogram = np.histogram(lbp, bins = range(2**8+1))[0]
X.append(histogram)
y.append(i)
X = preprocessing.scale(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
clf_list = [LogisticRegression(), SVC()]
clf_name = ['LR', 'SVC']
C_range = 10.0 ** np.arange(-5, 0)
for clf,name in zip(clf_list, clf_name):
for C in C_range:
for penalty in ["l1", "l2"]:
clf.C = C
clf.penalty = penalty
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
score = accuracy_score(y_test, y_pred)
print ("Accuracy for C = %.2e and penalty = %s is %.3f" % (C, penalty, score))
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] | 2.204615 | 650 |
# coding: utf-8
# Import dos módulos necessários e declaração de constantes
# ---
# In[1]:
import numpy as np
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from sklearn import metrics
from sklearn.model_selection import train_test_split
# quantidade de canais que as imagens de entrada possuem
# escalar cor de cinza, entao 1
CHANNEL = 1
# valor da largura e da altura das imagens de entrada
WIDTH_HEIGHT = 20
# tamanho do batch
BATCH = 128
# Load do arquivo de dados
# ---
# In[2]:
load = np.loadtxt('exdata.csv', delimiter=',')
# cada coluna tem um padrao de digito
data = load[:-1].T
# a ultima linha eh a classificacao do digito
result = load[-1]
# digito 0 corresponde ao valor 10
result[result == 10] = 0
# Pré-processamento dos dados e das classes
# ---
# In[3]:
# trasformar cada linha (digito) em uma matriz 20 x 20 x 1
data = data.reshape(data.shape[0], WIDTH_HEIGHT, WIDTH_HEIGHT, CHANNEL)
# converte array de 1 dimensao para uma matriz de dimensao 10
# ou seja, criar 10 classes, uma para cada digito possivel
result = keras.utils.to_categorical(result, 10)
# Separação dos dados em treinamento e teste
# ---
# In[4]:
in_train, in_test, out_train, out_test = train_test_split(data,
result,
test_size=(25/100),
train_size=(75/100))
# Definição da arquitetura da rede neural
# ---
# In[5]:
# 'pilha' de camadas lineares
model = Sequential()
# primeira camada precisa saber o que espera de entrada
# Conv2D cria uma camada de 'convolution' (add cada elemento da imagem com o seu vizinho local)
# isso eh feito atraves do input_shape
# 32 eh o numero de filtros
# relu = rectified linear unit
model.add(Conv2D(32,
kernel_size=(3, 3),
activation='relu',
input_shape=(WIDTH_HEIGHT, WIDTH_HEIGHT, CHANNEL)))
model.add(Conv2D(64, (3, 3), activation='relu'))
# MaxPooling2D cria uma camada que faz um processo de discretizacao baseada em amostra
model.add(MaxPooling2D(pool_size=(2, 2)))
# Dropout cria uma camada de regularizacao
# 0.25 eh a fracao da quantidade de entrada que entrara na camada
model.add(Dropout(0.25))
# Flatten cria uma camada que 'flatteniza'
model.add(Flatten())
# Dense cria uma camada que representa uma multiplicacao de matrizes
# 128 eh a dimensionalidade da saida
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
# Compilação da rede neural
# ---
# In[6]:
# configuracao de que como sera o aprendizado de processo
# para qualquer problema de classificacao deve-se usar o accuracy
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
# Input dos dados de treinamento
# ---
# In[7]:
# batch_size eh o numero de amostras por update do gradiente
# epochs = um epoch eh uma iteracao sobre os dados fornecidos
model.fit(in_train, out_train,
batch_size=BATCH,
epochs=10,
verbose=1,
validation_data=(in_test, out_test))
# Avaliação com os dados de teste
# ---
# In[8]:
# valores do modelo no modo de teste
score = model.evaluate(in_test, out_test, verbose=0)
print('Teste loss:', score[0])
print('Teste acurácia:', score[1])
# Matriz de confusão
# ---
# In[9]:
# gera a predicao para o conjunto de teste
prediction = model.predict(in_test, batch_size=BATCH, verbose=0)
# ajuste dado para ter info correta
prediction_classes = np.argmax(prediction, axis=1)
out_test_classes = np.argmax(out_test, axis=1)
# gera a 'confusion matrix'
matrix = metrics.confusion_matrix(out_test_classes , prediction_classes)
print(matrix)
# In[10]:
# gera um relatorio com as principais metricas da classificacao
report = metrics.classification_report(out_test_classes, prediction_classes)
print(report)
# Precisão final
# ---
# In[11]:
# calcula a precisao da classificacao
value = metrics.accuracy_score(out_test_classes, prediction_classes)
print("Precisão no conjunto de teste: {:.2%}".format(value))
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] | 2.391496 | 1,811 |
from boa3.builtin import contract
@contract('0x0102030405060708090A0B0C0D0E0F1011121314')
| [
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34,
15,
35,
15,
36,
15,
37,
8784,
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1415,
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198
] | 2.190476 | 42 |
from pymongo import MongoClient
from umongo import Instance
from umongo import Document, EmbeddedDocument
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm.session import sessionmaker, Session
from .standard_logger import Logger
from .consts import *
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13,
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198,
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] | 3.736842 | 76 |
#!/usr/bin/python
#
# Pickle deserialization RCE payload.
# To be invoked with command to execute at it's first parameter.
# Otherwise, the default one will be used.
#
import pickle
with open('rce.png', 'rb') as inF:
pickle.load(inF)
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'''
Created on Feb 1, 2014
@author: efarhan
'''
from engine.const import log
from engine.init import engine
from render_engine.input import input_manager
from engine.vector import Vector2
from input.mouse_input import get_mouse, show_mouse
from game_object.game_object_main import GameObject
from game_object.text import Text
from json_export.level_json import save_level
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#####################################################
# Copyright (c) 2014 Diogo Barradas #
# #
# See the file LICENSE.txt for copying permission. #
#####################################################
#!/usr/bin/env python
import argparse
import sys
import socket
import scanner
from scanner import scan
parser = argparse.ArgumentParser(prog='rainfall')
parser.add_argument('--version', action='version', version='%(prog)s 1.0')
parser.add_argument('-p', '--ports', nargs=2, required=True, help='Port interval to scan')
parser.add_argument('-t', '--target', required=True, help='Target host')
parser.add_argument('-m', '--mode', nargs=1, required=True, help='scan mode: 1-syn, 2-xmas, 3-fin, 4-null, 5-ack ')
args = parser.parse_args()
try:
beginPort = int(args.ports[0])
endPort = int(args.ports[1])
assert beginPort > 0 and endPort > 0 and beginPort <= endPort
except AssertionError:
print "[ERROR] Port range is invalid - startPort must be <= endPort, both of which > 0"
sys.exit()
target = args.target
mode = args.mode
scan(target, beginPort, endPort, int(mode[0])) | [
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] | 2.787589 | 419 |
from argparse import ArgumentParser
| [
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] | 4.75 | 8 |
from execicio110 import moedas
from time import sleep
p = float(input('Digite um preço: R$ '))
#1º preço /2º aumenta / 3º diminui
moedas.resumo(p,10, 20)
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#!/usr/bin/python
# Filename: autopatch.py
### File Information ###
"""
Patch the files automatically based on the autopatch.xsd.
Usage: $shell autopatch.py [PATCH_XML]
- PATCH_XML : The patch XML definition. Default to be bringup.xml
"""
__author__ = '[email protected] (duanqz)'
import os.path
import shutil
import sys
import fnmatch
import traceback
from diff_patch import DiffPatch
from xml_patch import Patcher as XMLPatcher
from target_finder import TargetFinder
from config import Config
from log import Log
from format import Format
try:
import xml.etree.cElementTree as ET
except ImportError:
import xml.etree.ElementTree as ET
# End of class AutoPatch
class Version:
""" Version control for file
"""
ANDROID_4_0 = 1
ANDROID_4_1 = ANDROID_4_0 << 1
ANDROID_4_2 = ANDROID_4_1 << 1
ANDROID_4_3 = ANDROID_4_2 << 1
ANDROID_4_4 = ANDROID_4_3 << 1
CURRENT_VERSION = ~0
@staticmethod
def parseCurrentVersion(patchXML):
""" Parse out current version from the patch XML
"""
try:
Version.CURRENT_VERSION = Version.parse(patchXML.getroot().attrib['version'])
except KeyError:
pass
@staticmethod
@staticmethod
class AutoPatchXML:
""" Represent the tree model of the patch XML.
"""
def parse(self):
""" Parse the XML with the schema defined in autopatch.xsd
"""
XMLDom = ET.parse(Config.PATCH_XML)
Version.parseCurrentVersion(XMLDom)
for feature in XMLDom.findall('feature'):
self.handleRevise(feature)
def handleRevise(self, feature):
""" Parse the revise node to handle the revise action.
"""
require = feature.attrib['require']
description = feature.attrib['description']
if self.needRevise(require):
Log.i("\n [%s]" % description)
for revise in feature:
ReviseExecutor(revise).run()
# End of class AutoPatchXML
class ReviseExecutor:
""" Execute revise action to a unique file.
Actions including ADD, MERGE, REPLACE.
"""
ADD = "ADD"
MERGE = "MERGE"
REPLACE = "REPLACE"
def __init__(self, revise):
""" @args revise: the revise XML node.
"""
self.action = revise.attrib['action']
# Compose the source and target file path
target = revise.attrib['target']
self.mOldSrc = os.path.join(Config.OLDER_DIR, target)
self.mNewSrc = os.path.join(Config.NEWER_DIR, target)
self.mTarget = TargetFinder().find(target)
# Initialize patch if defined
try:
patch = revise.attrib['patch']
self.mPatch = os.path.join(Config.PATCH_XML_DIR, patch)
except KeyError:
self.mPatch = None
# Initialize version if defined
try:
self.mVersion = revise.attrib['version']
except KeyError:
self.mVersion = None
def replaceOrAddSingleFile(self, source, target):
""" Add a file from source to target.
Replace the target if exist.
"""
if not os.path.exists(source):
Log.fail("File not exist: " + source)
return
if os.path.exists(target):
Log.i(" REPLACE " + target)
else:
Log.i(" ADD " + target)
self.createIfNotExist(os.path.dirname(target))
action = Format.REMOVE_LINE | Format.ACCESS_TO_NAME | Format.RESID_TO_NAME
formatSource = Format(Config.NEWER_DIR, source).do(action)
formatTarget = Format(Config.PRJ_ROOT, target).do(action)
shutil.copy(source, target)
formatSource.undo()
formatTarget.undo()
# End of class ReviseExecutor
# End of class Log
if __name__ == "__main__":
Config.setup(sys.argv[1:])
AutoPatch()
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] | 2.341757 | 1,662 |
import discord
from discord.ext import commands
import random, asyncio, aiohttp, datetime
| [
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] | 3.68 | 25 |
"""
Main program to run the detection
"""
from argparse import ArgumentParser
import cv2
import mediapipe as mp
import numpy as np
# for TCP connection with unity
import socket
from collections import deque
from platform import system
# face detection and facial landmark
from facial_landmark import FaceMeshDetector
# pose estimation and stablization
from pose_estimator import PoseEstimator
from stabilizer import Stabilizer
# Miscellaneous detections (eyes/ mouth...)
from facial_features import FacialFeatures, Eyes
# global variable
port = 5066 # have to be same as unity
# init TCP connection with unity
# return the socket connected
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--cam", type=int,
help="specify the camera number if you have multiple cameras",
default=0)
parser.add_argument("--connect", action="store_true",
help="connect to unity character",
default=False)
parser.add_argument("--debug", action="store_true",
help="showing the camera's image for debugging",
default=False)
args = parser.parse_args()
# demo code
main()
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] | 2.576471 | 510 |
#!/bin/env python
from __future__ import print_function
import sys
fileName = sys.argv[1]
f = open(fileName,'r')
messageToIgnore = [
'edm::FunctorTask'
,'edm::FunctorWaitingTask'
,'edm::ModuleCallingContext::setContext'
,'edm::SerialTaskQueue::'
,'edm::SerialTaskQueueChain::'
,'edm::WaitingTaskList'
,'edm::Worker::RunModuleTask<'
,'edm::Worker::beginStream'
,'edm::eventsetup::EventSetupRecord::getFromProxy'
,'edm::GlobalSchedule::processOneGlobalAsync'
,'edm::SerialTaskQueueChain::push'
,'edm::Worker::doWorkNoPrefetchingAsync'
,'edm::ServiceRegistry::setContext'
,'edm::ServiceRegistry::presentToken()'
,'edm::service::InitRootHandlers::ThreadTracker::on_scheduler_entry'
,'__TBB_machine_fetchstore4'
,'__TBB_machine_cmpswp4'
,'__TBB_machine_fetchstore1'
,'acquire (spin_rw_mutex.h:118)'
,'reset_extra_state ('
,'priority (scheduler_common.h:130)'
,'edm::service::MessageLogger::'
# ,'edm::service::MessageLogger::establishModule('
# ,'edm::Run::Run(edm::RunPrincipal const&,'
,'edm::service::MessageLogger::unEstablishModule'
,'tbb::'
,'edm::RunForOutput::RunForOutput('
,'edm::stream::EDAnalyzerAdaptor<'
,'edm::EventSetup::find('
,'edm::eventsetup::EventSetupRecord::find('
,'edm::eventsetup::DataKey::operator<('
,'edm::eventsetup::SimpleStringTag::operator<('
,'std::__shared_ptr<edm::serviceregistry::ServicesManager'
,'try_acquire (spin_mutex.h:109)'
,'edm::Run::Run('
,'FastTimerService::preStreamBeginRun('
,'decltype ({parm#1}()) edm::convertException::wrap<bool edm::Worker::runModule'
,'edm::Worker::reset()'
,'edm::stream::ProducingModuleAdaptorBase<edm::stream::EDProducerBase>::doStreamBeginLuminosityBlock('
,'edm::stream::ProducingModuleAdaptorBase<edm::stream::EDFilterBase>::doStreamBeginLuminosityBlock('
,'edm::LuminosityBlock::LuminosityBlock(edm::LuminosityBlockPrincipal'
,'edm::StreamSchedule::processOneStreamAsync<'
,'edm::Worker::doWorkAsync<'
,'edm::StreamSchedule::processOneEventAsync('
,'edm::ParentContext::moduleCallingContext()'
,'edm::ModuleCallingContext::getTopModuleCallingContext'
,'edm::Event::Event('
,'edm::Path::workerFinished('
,'edm::Path::updateCounters('
,'edm::Path::recordStatus('
,'FastTimerService::postPathEvent('
,'edm::hash_detail::isCompactForm_('
,'edm::InputProductResolver::resolveProduct_'
,'edm::NoProcessProductResolver::dataValidFromResolver('
,'edm::DataManagingProductResolver::productWasFetchedAndIsValid_(bool)'
,'FastTimerService::postModuleEvent('
,'edm::UnscheduledProductResolver::prefetchAsync_'
# ,'edm::NoProcessProductResolver::prefetchAsync_'
# ,'edm::NoProcessProductResolver::resolveProduct_('
,'edm::NoProcessProductResolver::'
,'reco::Jet::detectorP4'
,'edm::EarlyDeleteHelper::moduleRan'
,'edm::clearLoggedErrorsSummary('
,'edm::ProductProvenanceRetriever::branchIDToProvenance('
,'HistogramProbabilityEstimator::probability' #protected by an atomic
,'edm::EventPrincipal::setLuminosityBlockPrincipal'
,'edm::DataManagingProductResolver::'
]
stackToIgnore = [
'edm::service::MessageLogger::'
,'edm::MessageSender::ErrorObjDeleter'
,'edm::Run::runPrincipal() const'
,'edm::WaitingTaskList::'
,'edm::EventProcessor::beginJob()'
,'edm::StreamSchedule::processOneEventAsync'
,'edm::WorkerManager::resetAll()'
,'edm::ParentageRegistry::insertMapped('
,'edm::one::EDFilterBase::doEvent('
,'edm::one::EDProducerBase'
,'edm::EventBase::triggerNames_'
,'edm::EDFilter::doEvent('
,'edm::EDAnalyzer::doEvent('
,'edm::one::OutputModuleBase::doEvent'
,'edm::EDProducer::doEvent'
,'edm::Principal::clearPrincipal'
,'edm::RootOutputFile::writeOne'
,'edm::PrincipalCache::deleteRun('
,'edm::eventsetup::EventSetupProvider::eventSetupForInstance'
,'edm::EventPrincipal::clearEventPrincipal()'
,'FastTimerService::Resources::operator+='
,'FastTimerService::preSourceEvent(edm::StreamID)'
,'edm::EventPrincipal::fillEventPrincipal('
,'edm::InputProductResolver::putProduct_('
]
addressesToIgnore = [
# 'edm::eventsetup::makeEventSetupProvider('
# ,' edm::eventsetup::DataProxy::get('
# ,'cond::createPayload<'
# ,'edm::pset::Registry::getMapped('
'is in a rw- anonymous segment' #not sure about this one
# ,'edm::RootFile::fillRunAuxiliary'
,'tbb::internal::arena::arena('
# ,'edm::EventPrincipal::fillEventPrincipal('
# ,'edm::Principal::addUnscheduledProduct('
# ,'edm::RootDelayedReader::getProduct_'
# ,'TBranchElement::GetEntry('
# ,'edm::Event::put<'
# ,'edm::stream::EDProducerAdaptorBase::doEvent'
# ,'edm::stream::EDFilterAdaptorBase::doEvent('
# ,'edm::EventProcessor::init(' #this may ignore too much, but needed to ignore member data of streams
# ,'edm::global::EDProducerBase::doEvent'
# ,'FastTimerService::postBeginJob()'
# ,'edm::EDProducer::doEvent('
# ,'_ZN3pat15PackedCandidate27covarianceParameterization_E'
# ,'edm::RootOutputFile::writeOne'
,'DQMStore::book'
,'L1TdeCSCTF::L1TdeCSCTF' #legacy
#,'MeasurementTrackerEventProducer::produce(' #MeasurementTrackerEvent ultimately hits edmNew::DetSetVector's lazy caching of DetSet which is supposed to be thread safe (but may not be?)
,'std::vector<reco::TrackExtra' #this is the cache in Ref
,'std::vector<reco::Track'
,'std::vector<reco::PFConversion'
]
addressesToIgnoreIfRead = [
'edm::eventsetup::makeEventSetupProvider('
,' edm::eventsetup::DataProxy::get('
,'cond::createPayload<'
,'edm::pset::Registry::getMapped('
# ,'is in a rw- anonymous segment' #not sure about this one
,'edm::RootFile::fillRunAuxiliary'
# ,'tbb::internal::arena::arena('
,'edm::EventPrincipal::fillEventPrincipal('
,'edm::Principal::addUnscheduledProduct('
,'edm::RootDelayedReader::getProduct_'
,'TBranchElement::GetEntry('
,'edm::Event::put<'
,'edm::stream::EDProducerAdaptorBase::doEvent'
,'edm::stream::EDFilterAdaptorBase::doEvent('
,'edm::EventProcessor::init(' #this may ignore too much, but needed to ignore member data of streams
,'edm::global::EDProducerBase::doEvent'
,'FastTimerService::postBeginJob()'
,'edm::EDProducer::doEvent('
,'_ZN3pat15PackedCandidate27covarianceParameterization_E'
,'edm::RootOutputFile::writeOne'
,'BSS segment'
,'bytes inside data symbol' #this shows the writes but will miss the reads
,'FSQ::HandlerTemplate' #some function statics
# ,'DQMStore::book'
,'TBufferFile::'
,'edm::service::MessageLogger::'
,'TClass::GetClass('
]
#startOfMessage ='-------------------'
endOfMessage ='-------------------'
startOfMessage = 'Possible data race'
startOfMessageLength = len(startOfMessage)
messageStarted = False
lineCount = 100
buffer = []
maxCount = 20
lookForAddress = False
foundAddress = False
addressCount = 100
possibleDataRaceRead = False
foundStartOfMessage = False
for l in f.readlines():
if l[:2] != '==':
continue
if l.find(endOfMessage) != -1:
foundAddress = False
addressCount = 100
if l.find(startOfMessage) != -1:
lookForAddress = False
foundAddress = False
possibleDataRaceRead = (l.find('data race during read') != -1)
if buffer:
#print buffer
print('---------------------')
for b in buffer:
print(b[:-1])
buffer=[l]
lineCount = 0
continue
# if lineCount == 2:
# if l.find('data race') == -1:
# buffer = []
# lineCount = 100
# possibleDataRaceRead = (l.find('data race during read') != -1)
if lineCount < maxCount:
skipThis = False
for i in stackToIgnore:
if l.find(i) != -1:
lineCount = 100
skipThis = True
buffer = []
break
if skipThis:
continue
buffer.append(l)
lineCount +=1
if ' at 0x' in l:
for i in messageToIgnore:
if l.find(i) != -1:
buffer = []
lineCount = 100
break
if lineCount == 100:
continue
if l.find('Address 0x') != -1:
lookForAddress = True
foundAddress = False
lineCount = 100
if lineCount == maxCount:
lookForAddress = True
foundAddress = False
lineCount = 100
if lookForAddress:
if l.find('Address 0x') != -1:
foundAddress = True
lookForAddress = False
addressCount = 0
lineCount = 100
if foundAddress:
addressCount +=1
if addressCount < maxCount:
buffer.append(l)
for i in addressesToIgnore:
if l.find(i) != -1:
buffer = []
foundAddress = False
addressCount = 100
break
if possibleDataRaceRead:
for i in addressesToIgnoreIfRead:
if l.find(i) != -1:
buffer = []
foundAddress = False
addressCount = 100
break
if l[-3:]=="== ":
foundAddress = False
addressCount = 100
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22417,
14881,
3712,
4598,
9237,
10786,
198,
220,
220,
220,
837,
6,
276,
76,
3712,
505,
3712,
1961,
11547,
2189,
14881,
6,
198,
220,
220,
220,
837,
6,
276,
76,
3712,
9237,
14881,
3712,
46284,
36690,
62,
6,
198,
220,
220,
220,
837,
6,
276,
76,
3712,
1961,
22417,
3712,
4598,
9237,
10786,
198,
220,
220,
220,
837,
6,
276,
76,
3712,
1961,
37702,
9107,
3712,
4598,
9237,
10786,
198,
220,
220,
220,
837,
6,
276,
76,
3712,
505,
3712,
26410,
26796,
14881,
3712,
4598,
9237,
6,
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220,
220,
220,
837,
6,
276,
76,
3712,
1961,
11547,
2189,
3712,
4598,
9237,
6,
198,
220,
220,
220,
837,
6,
276,
76,
3712,
42904,
8521,
3712,
20063,
42904,
8521,
6,
198,
220,
220,
220,
837,
6,
276,
76,
3712,
30016,
26410,
8979,
3712,
13564,
3198,
6,
198,
220,
220,
220,
837,
6,
276,
76,
3712,
42904,
8521,
30562,
3712,
33678,
10987,
10786,
198,
220,
220,
220,
837,
6,
276,
76,
3712,
31534,
316,
929,
3712,
9237,
40786,
29495,
3712,
15596,
40786,
1890,
33384,
6,
198,
220,
220,
220,
837,
6,
276,
76,
3712,
9237,
42904,
8521,
3712,
20063,
9237,
42904,
8521,
3419,
6,
198,
220,
220,
220,
837,
6,
22968,
48801,
16177,
3712,
33236,
3712,
46616,
10,
11639,
198,
220,
220,
220,
837,
6,
22968,
48801,
16177,
3712,
3866,
7416,
9237,
7,
276,
76,
3712,
12124,
2389,
33047,
198,
220,
220,
220,
837,
6,
276,
76,
3712,
9237,
42904,
8521,
3712,
20797,
9237,
42904,
8521,
10786,
198,
220,
220,
220,
837,
6,
276,
76,
3712,
20560,
15667,
4965,
14375,
3712,
1996,
15667,
62,
10786,
198,
60,
198,
198,
2860,
16746,
2514,
32916,
382,
796,
685,
198,
2,
220,
220,
220,
705,
276,
76,
3712,
31534,
316,
929,
3712,
15883,
9237,
40786,
29495,
10786,
198,
2,
220,
220,
220,
837,
6,
1225,
76,
3712,
31534,
316,
929,
3712,
6601,
44148,
3712,
1136,
10786,
198,
2,
220,
220,
220,
837,
6,
17561,
3712,
17953,
19197,
2220,
27,
6,
198,
2,
220,
220,
220,
837,
6,
276,
76,
3712,
862,
316,
3712,
8081,
4592,
3712,
1136,
44,
6320,
10786,
198,
220,
220,
220,
705,
271,
287,
257,
374,
86,
12,
11614,
10618,
6,
1303,
1662,
1654,
546,
428,
530,
198,
2,
220,
220,
220,
837,
6,
276,
76,
3712,
30016,
8979,
3712,
20797,
10987,
32,
2821,
28129,
6,
198,
220,
220,
220,
837,
6,
83,
11848,
3712,
32538,
3712,
533,
2616,
3712,
533,
2616,
10786,
198,
2,
220,
220,
220,
837,
6,
276,
76,
3712,
9237,
42904,
8521,
3712,
20797,
9237,
42904,
8521,
10786,
198,
2,
220,
220,
220,
837,
6,
276,
76,
3712,
42904,
8521,
3712,
2860,
3118,
1416,
704,
6309,
15667,
10786,
198,
2,
220,
220,
220,
837,
6,
276,
76,
3712,
30016,
13856,
16548,
33634,
3712,
1136,
15667,
62,
6,
198,
2,
220,
220,
220,
837,
6,
22737,
25642,
20180,
3712,
3855,
30150,
10786,
198,
2,
220,
220,
220,
837,
6,
276,
76,
3712,
9237,
3712,
1996,
27,
6,
198,
2,
220,
220,
220,
837,
6,
276,
76,
3712,
5532,
3712,
1961,
11547,
2189,
48003,
273,
14881,
3712,
4598,
9237,
6,
198,
2,
220,
220,
220,
837,
6,
276,
76,
3712,
5532,
3712,
1961,
22417,
48003,
273,
14881,
3712,
4598,
9237,
10786,
198,
2,
220,
220,
220,
837,
6,
276,
76,
3712,
9237,
18709,
273,
3712,
15003,
10786,
1303,
5661,
743,
8856,
1165,
881,
11,
475,
2622,
284,
8856,
2888,
1366,
286,
15190,
198,
2,
220,
220,
220,
837,
6,
276,
76,
3712,
20541,
3712,
1961,
11547,
2189,
14881,
3712,
4598,
9237,
6,
198,
2,
220,
220,
220,
837,
6,
22968,
48801,
16177,
3712,
7353,
44140,
33308,
3419,
6,
198,
2,
220,
220,
220,
837,
6,
276,
76,
3712,
1961,
11547,
2189,
3712,
4598,
9237,
10786,
198,
2,
220,
220,
220,
837,
6,
62,
57,
45,
18,
8071,
1314,
47,
6021,
41572,
20540,
1983,
66,
709,
2743,
590,
36301,
1634,
62,
36,
6,
198,
2,
220,
220,
220,
837,
6,
276,
76,
3712,
30016,
26410,
8979,
3712,
13564,
3198,
6,
198,
220,
220,
220,
837,
6,
35,
48,
44,
22658,
3712,
2070,
6,
198,
220,
220,
220,
837,
6,
43,
16,
51,
2934,
7902,
4177,
37,
3712,
43,
16,
51,
2934,
7902,
4177,
37,
6,
1303,
1455,
1590,
198,
220,
220,
220,
1303,
4032,
47384,
434,
35694,
9237,
11547,
2189,
3712,
18230,
344,
10786,
1303,
47384,
434,
35694,
9237,
6165,
7127,
1225,
76,
3791,
3712,
11242,
7248,
38469,
338,
16931,
40918,
286,
4614,
7248,
543,
318,
4385,
284,
307,
4704,
3338,
357,
4360,
743,
407,
307,
10091,
198,
220,
220,
220,
837,
6,
19282,
3712,
31364,
27,
260,
1073,
3712,
24802,
27726,
6,
1303,
5661,
318,
262,
12940,
287,
6524,
198,
220,
220,
220,
837,
6,
19282,
3712,
31364,
27,
260,
1073,
3712,
24802,
6,
198,
220,
220,
220,
837,
6,
19282,
3712,
31364,
27,
260,
1073,
3712,
42668,
3103,
9641,
6,
198,
60,
198,
198,
2860,
16746,
2514,
32916,
382,
1532,
5569,
796,
685,
198,
220,
220,
220,
705,
276,
76,
3712,
31534,
316,
929,
3712,
15883,
9237,
40786,
29495,
10786,
198,
220,
220,
220,
837,
6,
1225,
76,
3712,
31534,
316,
929,
3712,
6601,
44148,
3712,
1136,
10786,
198,
220,
220,
220,
837,
6,
17561,
3712,
17953,
19197,
2220,
27,
6,
198,
220,
220,
220,
837,
6,
276,
76,
3712,
862,
316,
3712,
8081,
4592,
3712,
1136,
44,
6320,
10786,
198,
2,
220,
220,
220,
837,
6,
271,
287,
257,
374,
86,
12,
11614,
10618,
6,
1303,
1662,
1654,
546,
428,
530,
198,
220,
220,
220,
837,
6,
276,
76,
3712,
30016,
8979,
3712,
20797,
10987,
32,
2821,
28129,
6,
198,
2,
220,
220,
220,
837,
6,
83,
11848,
3712,
32538,
3712,
533,
2616,
3712,
533,
2616,
10786,
198,
220,
220,
220,
837,
6,
276,
76,
3712,
9237,
42904,
8521,
3712,
20797,
9237,
42904,
8521,
10786,
198,
220,
220,
220,
837,
6,
276,
76,
3712,
42904,
8521,
3712,
2860,
3118,
1416,
704,
6309,
15667,
10786,
198,
220,
220,
220,
837,
6,
276,
76,
3712,
30016,
13856,
16548,
33634,
3712,
1136,
15667,
62,
6,
198,
220,
220,
220,
837,
6,
22737,
25642,
20180,
3712,
3855,
30150,
10786,
198,
220,
220,
220,
837,
6,
276,
76,
3712,
9237,
3712,
1996,
27,
6,
198,
220,
220,
220,
837,
6,
276,
76,
3712,
5532,
3712,
1961,
11547,
2189,
48003,
273,
14881,
3712,
4598,
9237,
6,
198,
220,
220,
220,
837,
6,
276,
76,
3712,
5532,
3712,
1961,
22417,
48003,
273,
14881,
3712,
4598,
9237,
10786,
198,
220,
220,
220,
837,
6,
276,
76,
3712,
9237,
18709,
273,
3712,
15003,
10786,
1303,
5661,
743,
8856,
1165,
881,
11,
475,
2622,
284,
8856,
2888,
1366,
286,
15190,
198,
220,
220,
220,
837,
6,
276,
76,
3712,
20541,
3712,
1961,
11547,
2189,
14881,
3712,
4598,
9237,
6,
198,
220,
220,
220,
837,
6,
22968,
48801,
16177,
3712,
7353,
44140,
33308,
3419,
6,
198,
220,
220,
220,
837,
6,
276,
76,
3712,
1961,
11547,
2189,
3712,
4598,
9237,
10786,
198,
220,
220,
220,
837,
6,
62,
57,
45,
18,
8071,
1314,
47,
6021,
41572,
20540,
1983,
66,
709,
2743,
590,
36301,
1634,
62,
36,
6,
198,
220,
220,
220,
837,
6,
276,
76,
3712,
30016,
26410,
8979,
3712,
13564,
3198,
6,
198,
220,
220,
220,
837,
6,
4462,
50,
10618,
6,
198,
220,
220,
220,
837,
6,
33661,
2641,
1366,
6194,
6,
1303,
5661,
2523,
262,
6797,
475,
481,
2051,
262,
9743,
198,
220,
220,
220,
837,
6,
10652,
48,
3712,
25060,
30800,
6,
1303,
11246,
2163,
1185,
873,
198,
2,
220,
220,
220,
837,
6,
35,
48,
44,
22658,
3712,
2070,
6,
198,
220,
220,
220,
837,
6,
22737,
13712,
8979,
3712,
6,
198,
220,
220,
220,
837,
6,
276,
76,
3712,
15271,
3712,
12837,
11187,
1362,
3712,
6,
198,
220,
220,
220,
837,
6,
51,
9487,
3712,
3855,
9487,
10786,
198,
60,
198,
198,
2,
9688,
5189,
12837,
796,
6,
1783,
6329,
6,
198,
437,
5189,
12837,
796,
6,
1783,
6329,
6,
198,
9688,
5189,
12837,
796,
705,
47,
4733,
1366,
3234,
6,
198,
9688,
5189,
12837,
24539,
796,
18896,
7,
9688,
5189,
12837,
8,
198,
20500,
10434,
276,
796,
10352,
198,
1370,
12332,
796,
1802,
198,
22252,
796,
17635,
198,
9806,
12332,
796,
1160,
198,
5460,
1890,
20231,
796,
10352,
198,
9275,
20231,
796,
10352,
198,
21975,
12332,
796,
1802,
198,
79,
4733,
6601,
35157,
5569,
796,
10352,
198,
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10434,
5189,
12837,
796,
10352,
198,
1640,
300,
287,
277,
13,
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6615,
33529,
198,
220,
220,
220,
611,
300,
58,
25,
17,
60,
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705,
855,
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198,
220,
220,
220,
220,
220,
220,
220,
2555,
198,
220,
220,
220,
611,
300,
13,
19796,
7,
437,
5189,
12837,
8,
14512,
532,
16,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1043,
20231,
796,
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198,
220,
220,
220,
220,
220,
220,
220,
2209,
12332,
796,
1802,
198,
220,
220,
220,
611,
300,
13,
19796,
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9688,
5189,
12837,
8,
14512,
532,
16,
25,
198,
220,
220,
220,
220,
220,
220,
220,
804,
1890,
20231,
796,
10352,
198,
220,
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10352,
198,
220,
220,
220,
220,
220,
220,
220,
1744,
6601,
35157,
5569,
796,
357,
75,
13,
19796,
10786,
7890,
3234,
1141,
1100,
11537,
14512,
532,
16,
8,
198,
220,
220,
220,
220,
220,
220,
220,
611,
11876,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
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11876,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
10786,
19351,
12,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
275,
287,
11876,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
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65,
58,
21912,
16,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
11876,
41888,
75,
60,
198,
220,
220,
220,
220,
220,
220,
220,
1627,
12332,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
2555,
198,
2,
220,
220,
220,
611,
1627,
12332,
6624,
362,
25,
198,
2,
220,
220,
220,
220,
220,
220,
220,
611,
300,
13,
19796,
10786,
7890,
3234,
11537,
6624,
532,
16,
25,
198,
2,
220,
220,
220,
220,
220,
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220,
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11876,
796,
17635,
198,
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1627,
12332,
796,
1802,
198,
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220,
220,
220,
220,
220,
220,
1744,
6601,
35157,
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796,
357,
75,
13,
19796,
10786,
7890,
3234,
1141,
1100,
11537,
14512,
532,
16,
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198,
220,
220,
220,
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1627,
12332,
1279,
3509,
12332,
25,
198,
220,
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220,
220,
220,
220,
220,
14267,
1212,
796,
10352,
198,
220,
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220,
220,
220,
220,
220,
329,
1312,
287,
8931,
2514,
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382,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
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13,
19796,
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72,
8,
14512,
532,
16,
25,
198,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
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1627,
12332,
796,
1802,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
14267,
1212,
796,
6407,
198,
220,
220,
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1,
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220,
220,
220,
2209,
12332,
796,
1802,
198
] | 2.265603 | 4,198 |
# P003
# The prime factors of 13_195 are 5, 7, 13, and 29
# What is the largest prime factor of the number 600_851_475_143
import math
from sympy import isprime
if __name__ == "__main__":
main()
| [
2,
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198,
2,
383,
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286,
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] | 2.885714 | 70 |
#!/usr/bin/env python3
import os
import argparse
import pandas as pd
if __name__ == '__main__':
main()
| [
2,
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] | 2.166667 | 60 |
"""This module contains functions that belong to multiple categories. For example,
concat can be used to concat strings, varbinary, arrays, etc.
"""
import functools
from typing import Any, ClassVar, List, Type
import attr
from treeno.base import PrintOptions
from treeno.datatypes import types as type_consts
from treeno.datatypes.builder import array, char, unknown, varbinary, varchar
from treeno.datatypes.conversions import (
STRING_TYPES,
common_supertype,
promote_varchar_to_char,
)
from treeno.expression import (
OPERATOR_PRECEDENCE,
Value,
value_attr,
wrap_literal_list,
)
from treeno.functions.base import Function, GenericFunction
@value_attr
# TODO: See expressions.py
OPERATOR_PRECEDENCE[Concatenate] = 5
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1961,
18310,
58,
3103,
9246,
268,
378,
60,
796,
642,
198
] | 2.964706 | 255 |
import os
from tests.poc.context_example.exception import ExampleError
| [
11748,
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13,
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13,
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62,
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13,
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] | 3.65 | 20 |
"""Used to classify if certain strings are words or contain words"""
# used to eliminate non whitespace separators
import re
import sqlite3
import os
def load_words():
"""Loads the words document into a set"""
with open('cryptoline_modules/words.txt') as word_file:
valid_words = set(word_file.read().lower().split())
return valid_words
def has_english(string: str):
"""True if a string (separated by anything) has an english word"""
# Replaces all non letter characters with spaces
not_letter = re.compile('[^0-9a-zA-Z]+')
string = not_letter.sub(' ', string)
# Determines if the current section of the string occurs in the word text file
for item in string.split(" "):
if execute_query_contains(item):
return True
return False
def verify_db_file():
"""Will error if db does not exist"""
try:
con = sqlite3.connect('file:cryptoline_modules/word_database.db?mode=rw', uri=True)
except:
print("Building database (One Time Operation)")
make_database()
print("Done")
return False
return True
def make_database():
"""One time database creation from words.txt file"""
db_connection = sqlite3.connect("cryptoline_modules/word_database.db")
db_cursor = db_connection.cursor()
db_cursor.execute('DROP TABLE IF EXISTS Words ')
db_cursor.execute('CREATE TABLE Words (valid_word TEXT)')
with open('cryptoline_modules/words.txt') as word_file:
valid_words = set(word_file.read().lower().split())
for item in valid_words:
db_cursor.execute('INSERT INTO Words VALUES (?)', (item,))
db_connection.commit()
db_connection.close()
def execute_query_contains(uknown_word: str):
"""Returns true if the string is contained within the valid_word """
db = sqlite3.connect("cryptoline_modules/word_database.db")
db_cursor = db.cursor()
db_cursor.execute("SELECT rowid FROM Words WHERE valid_word = ?", (uknown_word.lower(),))
result = db_cursor.fetchone()
if result is not None:
return True
else:
return False
db.close()
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] | 2.785436 | 769 |
"""Testing the StringEnum class."""
import ezenum as eze
def test_basic():
"""Just check it out."""
rgb = eze.StringEnum(['Red', 'Green', 'Blue'])
assert rgb.Red == 'Red'
assert rgb.Green == 'Green'
assert rgb.Blue == 'Blue'
assert rgb[0] == 'Red'
assert rgb[1] == 'Green'
assert rgb[2] == 'Blue'
assert len(rgb) == 3
assert repr(rgb) == "['Red', 'Green', 'Blue']"
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] | 2.414201 | 169 |
from __future__ import annotations
import yaml
from importlib.resources import read_text
from ...config.configuration import Metric, MetricConfiguration
from ...config.junos import JunosMetricConfiguration
from ...devices import junosdevice
from .. import junos
from ..base import Collector
from . import base
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] | 4.173333 | 75 |
# -*- coding: utf-8 -*-
# Copyright (c) 2013, Sergio Callegari
# All rights reserved.
# This file is part of PyDSM.
# PyDSM is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# PyDSM is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with PyDSM. If not, see <http://www.gnu.org/licenses/>.
"""
ISO 226 loudness countours (:mod:`pysdm.iso226`)
================================================
Loudness contours from ISO 226.
Contours are returned both as tables of data and as contour functions
.. currentmodule:: pydsm.iso226
Functions returning ISO 226 contours
------------------------------------
.. autosummary::
:toctree: generated/
iso226_spl_contour -- Equal loudness contour (tabled)
iso226_spl_itpl -- Interpolated equal loudness contour
Functions computing loudness/acoustic pressure
----------------------------------------------
.. autosummary::
:toctree: generated/
tabled_L_p -- Return table of sound pressure levels for given loudness
tabled_L_N -- Return table of perceived loudness for given sound pressure
Functions returning data tabled in the standard
-----------------------------------------------
.. autosummary::
:toctree: generated/
tabled_f -- Return table of frequencies in ISO 226
tabled_alpha_f -- Return table of exponents for loudness perception
tabled_L_U -- Return table of magnitudes of the linear transfer function
tabled_T_f -- Return table of thresholds of hearing
Notes
-----
This module uses data from the latest revision of ISO 226 [1]_.
For reference, also consider [2]_.
The ISO standard provides the equal loudness contours as tabled data.
Tables end at 12.5 kHz. Above this frequency equal-loudness-level data are
relatively scarce and tend to be variable [3]_. Yet, it is known
that the human ear has a precipitous decline in sensitivity with increasing
frequency above 15 kHz, to the point that at about 20 kHz the percieved sound
becomes negligible (> 100 dB attenuation) [4]_. For this reason,
this module includes the possibility of delivering some modified ISO contours
where the tabled data is augmented by creating a new data point at 20 kHz
where the behavior found at 20 Hz is replicated.
.. [1] ISO 226:2003 "Acoustics - Normal equal-loudness-level contours"
.. [2] Jeff Tackett, "ISO 226 Equal-Loudness-Level Contour Signal,"
2005
(http://www.mathworks.com/matlabcentral/fileexchange/7028)
.. [3] Yoiti Suzuki et al, "Precise and Full-range Determination of
Two-dimensional Equal Loudness Contours," 2003
(http://www.nedo.go.jp/itd/grant-e/report/00pdf/is-01e.pdf)
.. [4] Robert A. Wannamaker "Psychoacoustically Optimal Noise
Shaping", J. Audio Eng. Soc., Vol. 40, N. 7/8, 1992 July/August
"""
from __future__ import division, print_function
import numpy as np
from scipy.interpolate import InterpolatedUnivariateSpline
__all__ = ["tabled_f", "tabled_alpha_f", "tabled_L_U", "tabled_T_f",
"tabled_L_p", "tabled_L_N",
"iso226_spl_contour", "iso226_spl_itpl"]
# Tabled ISO 226 parameters
tbl_f = np.asarray(
[20, 25, 31.5, 40, 50, 63, 80, 100, 125, 160, 200, 250, 315, 400,
500, 630, 800, 1000, 1250, 1600, 2000, 2500, 3150, 4000, 5000, 6300,
8000, 10000, 12500])
tbl_alpha_f = np.asarray(
[0.532, 0.506, 0.480, 0.455, 0.432, 0.409, 0.387, 0.367, 0.349, 0.330,
0.315, 0.301, 0.288, 0.276, 0.267, 0.259, 0.253, 0.250, 0.246, 0.244,
0.243, 0.243, 0.243, 0.242, 0.242, 0.245, 0.254, 0.271, 0.301])
tbl_L_U = np.asarray(
[-31.6, -27.2, -23.0, -19.1, -15.9, -13.0, -10.3, -8.1, -6.2, -4.5,
-3.1, -2.0, -1.1, -0.4, 0.0, 0.3, 0.5, 0.0, -2.7, -4.1, -1.0, 1.7,
2.5, 1.2, -2.1, -7.1, -11.2, -10.7, -3.1])
tbl_T_f = np.asarray(
[78.5, 68.7, 59.5, 51.1, 44.0, 37.5, 31.5, 26.5, 22.1, 17.9, 14.4,
11.4, 8.6, 6.2, 4.4, 3.0, 2.2, 2.4, 3.5, 1.7, -1.3, -4.2, -6.0, -5.4,
-1.5, 6.0, 12.6, 13.9, 12.3])
def tabled_f(hfe=False):
"""Table of frequencies in ISO 226.
Parameters
----------
hfe : bool
whether the table should be augmented with a data point
at 20 kHz (High-Frequency-Enhanced table)
Returns
-------
f : array of floats
the frequency table.
"""
return np.append(tbl_f, 20E3) if hfe else tbl_f
def tabled_alpha_f(hfe=False):
"""Table of exponents for loudness perception in ISO 226.
Parameters
----------
hfe : bool
whether the table should be augmented with a data point
at 20 kHz (High-Frequency-Enhanced table)
Returns
-------
alpha_f : array of floats
the exponents table.
"""
return np.append(tbl_alpha_f, tbl_alpha_f[0]) if hfe else tbl_alpha_f
def tabled_L_U(hfe=False):
"""Table of magnitudes of the linear transfer function in ISO 226.
Parameters
----------
hfe : bool
whether the table should be augmented with a data point
at 20 kHz (High-Frequency-Enhanced table)
Returns
-------
L_U : array of floats
the magnitudes table.
Notes
-----
The returned values are the magnitude of the linear transfer function
normalized at 1 kHz.
"""
return np.append(tbl_L_U, tbl_L_U[0]) if hfe else tbl_L_U
def tabled_T_f(hfe=False):
"""Table of thresholds of hearing in ISO 226.
Parameters
----------
hfe : bool
whether the table should be augmented with a data point
at 20 kHz (High-Frequency-Enhanced table)
Returns
-------
T_f : array of floats
the thresholds table.
"""
return np.append(tbl_T_f, tbl_T_f[0]) if hfe else tbl_T_f
# Check that it works fine when L_N is array
def tabled_A_f(L_N, hfe=False):
"""Table of A_f values for given loundess in ISO 226.
Parameters
----------
L_N : float
percieved loudness level in phons
hfe : bool
whether the table should be augmented with a data point
at 20 kHz (High-Frequency-Enhanced table)
Returns
-------
A_f : array of floats
the A_f table.
Notes
-----
1 phon is 1 dB_SPL (sound pressure level) at 1 kHz. Sound pressure levels
are measured in dBs by referring to a reference pressure level P0 (close
to the hearing threshold at 1 kHz and set to 20 uPa RMS).
"""
A_f = (4.47E-3*(10.0**(0.025*L_N)-1.15) +
(0.4*10.0**((tbl_T_f+tbl_L_U)/10.0-9.0))**tbl_alpha_f)
return np.append(A_f, A_f[0]) if hfe else A_f
# Check that it works fine when L_N is array
def tabled_L_p(L_N, hfe=False):
"""Table of sound pressure levels for given loudness in ISO 226.
This function returns a table according to ISO 226 sect 4.1.
Parameters
----------
L_N : float
percieved loudness level in phons
hfe : bool
whether the table should be augmented with a data point
at 20 kHz (High-Frequency-Enhanced table)
Returns
-------
L_p : array of floats
the sound pressure level table. Sound pressure levels are returned
in DB_SPL
Notes
-----
1 phon is 1 dB_SPL (sound pressure level) at 1 kHz. Sound pressure levels
are measured in dBs by referring to a reference pressure level P0 (close
to the hearing threshold at 1 kHz and set to 20 uPa RMS).
"""
L_p = (10.0/tbl_alpha_f)*np.log10(tabled_A_f(L_N))-tbl_L_U + 94.0
return np.append(L_p, L_p[0]) if hfe else L_p
# Check that it works fine when L_P is array
def tabled_B_f(L_p, hfe=False):
"""Table of B_f values for given sound pressure in ISO 226.
Parameters
----------
L_p : float
sound pressure level in dB_SPL
hfe : bool
whether the table should be augmented with a data point
at 20 kHz (High-Frequency-Enhanced table)
Returns
-------
B_f : array of floats
the B_f table.
Notes
-----
Sound pressure levels are measured in dBs by referring to a
reference pressure level P0 (close to the hearing threshold at 1 kHz and
set to 20 uPa RMS).
"""
B_f = ((0.4*10**(L_p+tbl_L_U)/10.-9.)**tbl_alpha_f -
(0.4*10**(tbl_T_f+tbl_L_U)/10.-9.)**tbl_alpha_f +
0.005135)
return np.append(B_f, B_f[0]) if hfe else B_f
# Check that it works fine when L_N is array
def tabled_L_N(L_p, hfe=False):
"""Table of perceived loudness levels for given sound pressure in ISO 226.
This function returns a table according to ISO 226 sect 4.2.
Parameters
----------
L_p : float
sound pressure level in dB_SPL
hfe : bool
whether the table should be augmented with a data point
at 20 kHz (High-Frequency-Enhanced table)
Returns
-------
L_N : array of floats
the perceived loudness level table. Loudness levels are returned
in phons
Notes
-----
1 phon is 1 dB_SPL (sound pressure level) at 1 kHz. Sound pressure levels
are measured in dBs by referring to a reference pressure level P0 (close
to the hearing threshold at 1 kHz and set to 20 uPa RMS).
"""
L_N = 40*np.log10(tabled_B_f(L_p))+94.0
return np.append(L_N, L_N[0]) if hfe else L_N
def iso226_spl_contour(L_N=40, hfe=False):
"""Generates an equal loudness contour as described in ISO 226.
This function returns the control points describing the equal loudness
contour for the input phon level, according to ISO 226 sect 4.1.
Parameters
----------
L_N : float, optional
perceived loudness level in phons.
hfe : bool
whether the table should be augmented with a data point
at 20 kHz (High-Frequency-Enhanced table)
Returns
-------
f : ndarray
frequencies where the loudness is evaluated.
spl : ndarray
equivalent sound pressure level at the frequencies f.
Notes
-----
1 phon is 1 dB_SPL (sound pressure level) at 1 kHz. Sound pressure levels
are measured in dBs by referring to a reference pressure level P0 (close
to the hearing threshold at 1 kHz and set to 20 uPa RMS).
The valid input phon range is 0-90 dB_SPL. Above 80 dB, only the
frequency range 20-4000 Hz is significant.
"""
# Check for valid input range
if L_N < 0 or L_N > 90:
raise ValueError('Parameter L_N out of bounds [0-90].')
# Derive sound pressure level from loudness level (ISO 226 sect 4.1)
return tabled_f(hfe), tabled_L_p(L_N, hfe)
def iso226_spl_itpl(L_N=40, hfe=False, k=3):
"""Generates an interpolation of an equal loudness contour.
This function returns an interpolation object describing the equal
loudness contour for the input phon level, according to ISO 226 sect 4.1.
Parameters
----------
L_N : float, optional
perceived loudness level in phons.
hfe : bool
whether the table should be augmented with a data point
at 20 kHz (High-Frequency-Enhanced table)
k : int
interpolation order
Returns
-------
itpl : univariate interpolation object
function-like object that takes a frequency f as its input and returns
the equivalent sound pressure level at f
Notes
-----
1 phon is 1 dB_SPL (sound pressure level) at 1 kHz. Sound pressure levels
are measured in dBs by referring to a reference pressure level P0 (close
to the hearing threshold at 1 kHz and set to 20 uPa RMS).
The valid input phon range is 0-90 dB_SPL. Above 80 dB, only the
frequency range 20-4000 Hz is significant.
"""
ff, yy = iso226_spl_contour(L_N, hfe)
return InterpolatedUnivariateSpline(ff, yy, k=k)
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] | 2.668518 | 4,501 |
import logging
import sys
from pyzabbix import ZabbixMetric, ZabbixSender
from mongodb_consistent_backup.Errors import NotifyError, OperationError
from mongodb_consistent_backup.Pipeline import Task
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import pickle
import os
from detectron2.utils.file_io import PathManager
from detectron2.checkpoint import DetectionCheckpointer
from mmcv.runner.checkpoint import (
_load_checkpoint,
load_state_dict,
_process_mmcls_checkpoint,
)
import math
import logging
from timm.models.helpers import load_state_dict_from_hf, load_state_dict_from_url, has_hf_hub, adapt_input_conv
_logger = logging.getLogger(__name__)
class MyCheckpointer(DetectionCheckpointer):
"""https://github.com/aim-
uofa/AdelaiDet/blob/master/adet/checkpoint/adet_checkpoint.py Same as
:class:`DetectronCheckpointer`, but is able to convert models in AdelaiDet,
such as LPF backbone."""
def load_timm_pretrained(
model,
default_cfg=None,
num_classes=1000,
in_chans=3,
filter_fn=None,
strict=True,
progress=True,
adapt_input_mode="custom",
):
"""Load pretrained checkpoint
Args:
model (nn.Module) : PyTorch model module
default_cfg (Optional[Dict]): default configuration for pretrained weights / target dataset
num_classes (int): num_classes for model
in_chans (int): in_chans for model
filter_fn (Optional[Callable]): state_dict filter fn for load (takes state_dict, model as args)
strict (bool): strict load of checkpoint
progress (bool): enable progress bar for weight download
"""
default_cfg = default_cfg or getattr(model, "default_cfg", None) or {}
pretrained_url = default_cfg.get("url", None)
hf_hub_id = default_cfg.get("hf_hub", None)
if not pretrained_url and not hf_hub_id:
_logger.warning("No pretrained weights exist for this model. Using random initialization.")
return
if hf_hub_id and has_hf_hub(necessary=not pretrained_url):
_logger.info(f"Loading pretrained weights from Hugging Face hub ({hf_hub_id})")
state_dict = load_state_dict_from_hf(hf_hub_id)
else:
_logger.info(f"Loading pretrained weights from url ({pretrained_url})")
state_dict = load_state_dict_from_url(pretrained_url, progress=progress, map_location="cpu")
if filter_fn is not None:
# for backwards compat with filter fn that take one arg, try one first, the two
try:
state_dict = filter_fn(state_dict)
except TypeError:
state_dict = filter_fn(state_dict, model)
input_convs = default_cfg.get("first_conv", None)
if input_convs is not None and in_chans != 3:
if isinstance(input_convs, str):
input_convs = (input_convs,)
for input_conv_name in input_convs:
weight_name = input_conv_name + ".weight"
try:
if adapt_input_mode == "timm":
state_dict[weight_name] = adapt_input_conv(in_chans, state_dict[weight_name])
_logger.warning(
f"Converted input conv {input_conv_name} pretrained weights from 3 to {in_chans} channel(s) using timm strategy"
)
else:
state_dict[weight_name] = my_adapt_input_conv(
in_chans, state_dict[weight_name], model_conv_weight=model.state_dict()[weight_name]
)
_logger.warning(
f"Converted input conv {input_conv_name} pretrained weights from 3 to {in_chans} channel(s) using custom strategy"
)
except NotImplementedError as e:
del state_dict[weight_name]
strict = False
_logger.warning(
f"Unable to convert pretrained {input_conv_name} weights, using random init for this layer."
)
classifiers = default_cfg.get("classifier", None)
label_offset = default_cfg.get("label_offset", 0)
if classifiers is not None:
if isinstance(classifiers, str):
classifiers = (classifiers,)
if num_classes != default_cfg["num_classes"]:
for classifier_name in classifiers:
# completely discard fully connected if model num_classes doesn't match pretrained weights
del state_dict[classifier_name + ".weight"]
del state_dict[classifier_name + ".bias"]
strict = False
elif label_offset > 0:
for classifier_name in classifiers:
# special case for pretrained weights with an extra background class in pretrained weights
classifier_weight = state_dict[classifier_name + ".weight"]
state_dict[classifier_name + ".weight"] = classifier_weight[label_offset:]
classifier_bias = state_dict[classifier_name + ".bias"]
state_dict[classifier_name + ".bias"] = classifier_bias[label_offset:]
model.load_state_dict(state_dict, strict=strict)
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2012,
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] | 2.339913 | 2,077 |
import os.path
import time
from array import LoxArray
from instance import Instance
from lox_callable import Callable
from lox_class import LoxClass
from exception import NativeException
| [
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] | 3.788462 | 52 |
import ast
from typing import Iterable, List, Tuple, Type, TypeVar
from .exceptions import EmptyBlock
from .helpers import filter_arrange_nodes, get_first_token, get_last_token
from .types import LineType
_Block = TypeVar('_Block', bound='Block')
class Block:
"""
An Arrange, Act or Assert block of code as parsed from the test function.
Note:
This may just become the Act Block *AND* since the Act Block is just a
single node, this might not even be required.
Args:
nodes: Nodes that make up this block.
line_type: Type of line that this blocks writes into the line markers
instance for the function.
Notes:
* Blocks with no nodes are allowed (at the moment).
"""
@classmethod
def build_act(cls: Type[_Block], node: ast.stmt) -> _Block:
"""
Act block is a single node.
"""
return cls([node], LineType.act)
@classmethod
def build_arrange(cls: Type[_Block], nodes: List[ast.stmt], act_block_first_line: int) -> _Block:
"""
Arrange block is all non-pass and non-docstring nodes before the Act
block start.
Args:
nodes: Body of test function / method.
act_block_first_line
"""
return cls(filter_arrange_nodes(nodes, act_block_first_line), LineType.arrange)
def get_span(self, first_line_no: int) -> Tuple[int, int]:
"""
Args:
first_line_no: First line number of Block. Used to calculate
relative line numbers.
Returns:
First and last line covered by this block, counted relative to the
start of the Function.
Raises:
EmptyBlock: when block has no nodes
"""
if not self.nodes:
raise EmptyBlock(f'span requested from {self.line_type} block with no nodes')
# start and end are (<line number>, <indent>) pairs, so just the line
# numbers are picked out.
return (
get_first_token(self.nodes[0]).start[0] - first_line_no,
get_last_token(self.nodes[-1]).end[0] - first_line_no,
)
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] | 2.419463 | 894 |
# Copyright 2015 The Chromium OS Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
"""Logging module to be used by all scripts.
cros_logging is a wrapper around logging with additional support for NOTICE
level. This is to be used instead of the default logging module. The new
logging level can only be used from here.
"""
from __future__ import print_function
import sys
# pylint: disable=unused-wildcard-import, wildcard-import
from logging import *
# pylint: enable=unused-wildcard-import, wildcard-import
# Have to import shutdown explicitly from logging because it is not included
# in logging's __all__.
# pylint: disable=unused-import
from logging import shutdown
# pylint: enable=unused-import
# Import as private to avoid polluting module namespace.
from chromite.lib import buildbot_annotations as _annotations
# Notice Level.
NOTICE = 25
addLevelName(NOTICE, 'NOTICE')
# Notice implementation.
def notice(message, *args, **kwargs):
"""Log 'msg % args' with severity 'NOTICE'."""
log(NOTICE, message, *args, **kwargs)
# Only buildbot aware entry-points need to spew buildbot specific logs. Require
# user action for the special log lines.
_buildbot_markers_enabled = False
def _PrintForBuildbot(handle, annotation_class, *args):
"""Log a line for buildbot.
This function dumps a line to log recognizable by buildbot if
EnableBuildbotMarkers has been called. Otherwise, it dumps the same line in a
human friendly way that buildbot ignores.
Args:
handle: The pipe to dump the log to. If None, log to sys.stderr.
annotation_class: Annotation subclass for the type of buildbot log.
buildbot_tag: A tag specifying the type of buildbot log.
*args: The rest of the str arguments to be dumped to the log.
"""
if handle is None:
handle = sys.stderr
# Cast each argument, because we end up getting all sorts of objects from
# callers.
str_args = [str(x) for x in args]
annotation = annotation_class(*str_args)
if _buildbot_markers_enabled:
line = str(annotation)
else:
line = annotation.human_friendly
handle.write('\n' + line + '\n')
def PrintBuildbotLink(text, url, handle=None):
"""Prints out a link to buildbot."""
_PrintForBuildbot(handle, _annotations.StepLink, text, url)
def PrintBuildbotStepText(text, handle=None):
"""Prints out stage text to buildbot."""
_PrintForBuildbot(handle, _annotations.StepText, text)
def PrintBuildbotStepWarnings(handle=None):
"""Marks a stage as having warnings."""
_PrintForBuildbot(handle, _annotations.StepWarnings)
def PrintBuildbotStepFailure(handle=None):
"""Marks a stage as having failures."""
_PrintForBuildbot(handle, _annotations.StepFailure)
def PrintBuildbotStepName(name, handle=None):
"""Marks a step name for buildbot to display."""
_PrintForBuildbot(handle, _annotations.BuildStep, name)
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602,
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15580,
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8,
198
] | 3.339408 | 878 |
# encoding: utf-8
from time import strptime
__author__ = "Patrick Lampe"
__email__ = "uni at lampep.de"
| [
2,
21004,
25,
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] | 2.634146 | 41 |
#!/usr/bin/env python3
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Copyright 2013 California Institute of Technology. ALL RIGHTS RESERVED.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# United States Government Sponsorship acknowledged. This software is subject to
# U.S. export control laws and regulations and has been classified as 'EAR99 NLR'
# (No [Export] License Required except when exporting to an embargoed country,
# end user, or in support of a prohibited end use). By downloading this software,
# the user agrees to comply with all applicable U.S. export laws and regulations.
# The user has the responsibility to obtain export licenses, or other export
# authority as may be required before exporting this software to any 'EAR99'
# embargoed foreign country or citizen of those countries.
#
# Author: Eric Gurrola
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
import collections
Tag = collections.namedtuple('Tag', 'version svn_revision yyyymmdd')
releases = (Tag('1.0.0', '739', '20120814'),
Tag('1.5.0', '1180', '20131018'),
Tag('1.5.01', '1191', '20131028'),
Tag('2.0.0', '1554', '20140724'),
Tag('2.0.0_201409', '1612', '20140918'),
Tag('2.0.0_201410', '1651', '20141103'),
Tag('2.0.0_201505', '1733', '20150504'),
Tag('2.0.0_201506', '1783', '20150619'),
Tag('2.0.0_201511', '1917', '20151123'),
Tag('2.0.0_201512', '1931', '20151221'),
Tag('2.0.0_201604', '2047', '20160426'),
Tag('2.0.0_201604_dempatch', '2118:2047', '20160727'),
Tag('2.0.0_201609', '2143', '20160903'),
Tag('2.0.0_20160906', '2145', '20160906'),
Tag('2.0.0_20160908', '2150', '20160908'),
Tag('2.0.0_20160912', '2153', '20160912'),
Tag('2.0.0_20170403', '2256', '20170403'),
Tag('2.1.0', '2366', '20170806'),
Tag('2.2.0', '2497', '20180714'),
Tag('2.2.1', '2517', '20181221'),
Tag('2.3', '2531', '20190112'),
# git migration
Tag('2.3.1', '', '20190220'),
Tag('2.3.2', '', '20190618'),
Tag('2.3.3', '', '20200402'),
Tag('2.4.0', '', '20200730'),
Tag('2.4.1', '', '20200915'),
Tag('2.4.2', '', '20201116'),
Tag('2.5.0', '', '20210304'),
Tag('2.5.1', '', '20210305'),
Tag('2.5.2', '', '20210528'),
)
release_version = releases[-1].version
release_svn_revision = releases[-1].svn_revision
release_date = releases[-1].yyyymmdd
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] | 2.366071 | 1,344 |
#@+leo-ver=5-thin
#@+node:ekr.20140726091031.18143: * @file writers/basewriter.py
'''A module defining the base class for all writers in leo.plugins.writers.'''
class BaseWriter:
'''The base writer class for all writers in leo.plugins.writers.'''
def __init__(self, c):
'''Ctor for leo.plugins.writers.BaseWriter.'''
self.c = c
self.at = c.atFileCommands
#@+others
#@+node:ekr.20150626092123.1: ** basewriter.put
def put(self, s):
'''Write line s using at.os, taking special care of newlines.'''
at = self.at
at.os(s[: -1] if s.endswith('\n') else s)
at.onl()
#@+node:ekr.20150626092140.1: ** basewriter.put_node_sentinel
def put_node_sentinel(self, p, delim, delim2=''):
'''Put an @+node sentinel for node p.'''
at = self.at
# Like at.nodeSentinelText.
gnx = p.v.fileIndex
level = p.level()
if level > 2:
s = "%s: *%s* %s" % (gnx, level, p.h)
else:
s = "%s: %s %s" % (gnx, '*' * level, p.h)
# Like at.putSentinel.
at.os('%s@+node:%s%s' % (delim, s, delim2))
at.onl()
#@+node:ekr.20161125140611.1: ** basewriter.split_lines
def split_lines(self, s):
'''Exactly the same as g.splitLines(s).'''
return s.splitlines(True) if s else []
# This is a Python string function!
#@-others
#@@language python
#@@tabwidth -4
#@-leo
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] | 2.02371 | 717 |
from pathlib import Path
from osgeo import ogr, gdal
import os
import geopandas as pd
from geopandas._vectorized import simplify
import rasterio
from rasterstats import zonal_stats
import time
import shutil
def intersection(ShpA, ShpB, fname):
"""
This function is used to get the intersection between shapefile A and shapefile B.
:param shpPath: the path of input shapefile A
:param roadShp: the path of input shapefile B
:param fname: the path of output shapefile
:return:
"""
driver = ogr.GetDriverByName("ESRI Shapefile")
dataSourceA = driver.Open(ShpA, 1)
layerA = dataSourceA.GetLayer()
dataSourceB = driver.Open(ShpB, 1)
layerB = dataSourceB.GetLayer()
# 新建DataSource，Layer
out_ds = driver.CreateDataSource(fname)
out_lyr = out_ds.CreateLayer(fname, layerA.GetSpatialRef(), ogr.wkbPolygon)
def_feature = out_lyr.GetLayerDefn()
# 遍历原始的Shapefile文件给每个Geometry做Buffer操作
# current_union = layer[0].Clone()
print('the length of layer:', len(layerA))
if len(layerA) == 0:
return
for featureA in layerA:
geometryA = featureA.GetGeometryRef()
for featureB in layerB:
geometryB = featureB.GetGeometryRef()
inter = geometryB.Intersection(geometryA).Clone()
out_feature = ogr.Feature(def_feature)
out_feature.SetGeometry(inter)
out_lyr.ResetReading()
out_lyr.CreateFeature(out_feature)
del dataSourceA, dataSourceB, out_ds
def MergeOneShp(inShp, outShp):
"""
merge all features in one shapefile
:param inShp: the path of input shapefile
:param outShp: the path of output shapefile
:return:
"""
driver = ogr.GetDriverByName("ESRI Shapefile")
dataSource = driver.Open(inShp, 1)
layer = dataSource.GetLayer()
# 新建DataSource，Layer
out_ds = driver.CreateDataSource(outShp)
out_lyr = out_ds.CreateLayer(outShp, layer.GetSpatialRef(), ogr.wkbPolygon)
def_feature = out_lyr.GetLayerDefn()
# 遍历原始的Shapefile文件给每个Geometry做Buffer操作
# current_union = layer[0].Clone()
print('the length of layer:', len(layer))
if len(layer) == 0:
return
for i, feature in enumerate(layer):
geometry = feature.GetGeometryRef()
if i == 0:
current_union = geometry.Clone()
current_union = current_union.Union(geometry).Clone()
if i == len(layer) - 1:
out_feature = ogr.Feature(def_feature)
out_feature.SetGeometry(current_union)
out_lyr.ResetReading()
out_lyr.CreateFeature(out_feature)
del dataSource, out_ds
def multipoly2singlepoly(inputshp, outputshp):
"""
multi part to single part
:param inputshp: the path of input shapefile
:param outputshp: the path of output shapefile
:return:
"""
gdal.UseExceptions()
driver = ogr.GetDriverByName('ESRI Shapefile')
in_ds = driver.Open(inputshp, 0)
in_lyr = in_ds.GetLayer()
if os.path.exists(outputshp):
driver.DeleteDataSource(outputshp)
out_ds = driver.CreateDataSource(outputshp)
out_lyr = out_ds.CreateLayer('poly', in_lyr.GetSpatialRef(), geom_type=ogr.wkbPolygon)
for in_feat in in_lyr:
geom = in_feat.GetGeometryRef()
if geom.GetGeometryName() == 'MULTIPOLYGON':
for geom_part in geom:
addPolygon(geom_part.ExportToWkb(), out_lyr)
else:
addPolygon(geom.ExportToWkb(), out_lyr)
del in_ds, out_ds
def remove_big_feature(inputShp, outputShp, area_threshold):
"""
This function is used to remove big area of feature from shapefile
:param inputShp: the path of input shapefile
:param outputShp: the path of output shapefile
:param area_thresold: the threshold of area
:return:
"""
driver = ogr.GetDriverByName("ESRI Shapefile")
dataSource = driver.Open(inputShp, 1)
layer = dataSource.GetLayer()
new_field = ogr.FieldDefn("Area", ogr.OFTReal)
new_field.SetWidth(32)
new_field.SetPrecision(16) # 设置面积精度,小数点后16位
layer.CreateField(new_field)
# 新建DataSource，Layer
out_ds = driver.CreateDataSource(outputShp)
out_lyr = out_ds.CreateLayer(outputShp, layer.GetSpatialRef(), ogr.wkbPolygon)
def_feature = out_lyr.GetLayerDefn()
for feature in layer:
geom = feature.GetGeometryRef()
area = geom.GetArea() # 计算面积
if area > area_threshold:
continue
feature.SetField("Area", area) # 将面积添加到属性表中
layer.SetFeature(feature)
out_feature = ogr.Feature(def_feature)
out_feature.SetGeometry(geom)
out_lyr.CreateFeature(out_feature)
out_feature = None
out_ds.FlushCache()
del dataSource, out_ds
def remove_small_feature(inputShp, outputShp, area_threshold):
"""
This function is used to remove small area of feature from shapefile
:param inputShp: the path of input shapefile
:param outputShp: the path of output shapefile
:param area_thresold: the threshold of area
:return:
"""
driver = ogr.GetDriverByName("ESRI Shapefile")
dataSource = driver.Open(inputShp, 1)
layer = dataSource.GetLayer()
new_field = ogr.FieldDefn("Area", ogr.OFTReal)
new_field.SetWidth(32)
new_field.SetPrecision(16) # 设置面积精度,小数点后16位
layer.CreateField(new_field)
# 新建DataSource，Layer
out_ds = driver.CreateDataSource(outputShp)
out_lyr = out_ds.CreateLayer(outputShp, layer.GetSpatialRef(), ogr.wkbPolygon)
def_feature = out_lyr.GetLayerDefn()
for feature in layer:
geom = feature.GetGeometryRef()
area = geom.GetArea() # 计算面积
if area < area_threshold:
continue
feature.SetField("Area", area) # 将面积添加到属性表中
layer.SetFeature(feature)
out_feature = ogr.Feature(def_feature)
out_feature.SetGeometry(geom)
out_lyr.CreateFeature(out_feature)
out_feature = None
out_ds.FlushCache()
del dataSource, out_ds
def buffer(inShp, outShp, bdistance=0.02):
"""
setting up buffer zone in shapefile
:param inShp: the path of input shapefile
:param outShp: the path of output shapefile
:param bdistance: the distance of buffer
:return:
"""
ogr.UseExceptions()
in_ds = ogr.Open(inShp)
in_lyr = in_ds.GetLayer()
# 创建输出Buffer文件
driver = ogr.GetDriverByName('ESRI Shapefile')
if Path(outShp).exists():
driver.DeleteDataSource(outShp)
# 新建DataSource，Layer
out_ds = driver.CreateDataSource(outShp)
out_lyr = out_ds.CreateLayer(outShp, in_lyr.GetSpatialRef(), ogr.wkbPolygon)
def_feature = out_lyr.GetLayerDefn()
# 遍历原始的Shapefile文件给每个Geometry做Buffer操作
for feature in in_lyr:
geometry = feature.GetGeometryRef()
buffer = geometry.Buffer(bdistance)
out_feature = ogr.Feature(def_feature)
out_feature.SetGeometry(buffer)
out_lyr.CreateFeature(out_feature)
out_feature = None
out_ds.FlushCache()
del in_ds, out_ds
def smoothing(inShp, fname, bdistance=0.001):
"""
:param inShp: the path of input shapefile
:param fname: the path of output shapefile
:param bdistance: the distance of buffer
:return:
"""
ogr.UseExceptions()
in_ds = ogr.Open(inShp)
in_lyr = in_ds.GetLayer()
# 创建输出Buffer文件
driver = ogr.GetDriverByName('ESRI Shapefile')
if Path(fname).exists():
driver.DeleteDataSource(fname)
# 新建DataSource，Layer
out_ds = driver.CreateDataSource(fname)
out_lyr = out_ds.CreateLayer(fname, in_lyr.GetSpatialRef(), ogr.wkbPolygon)
def_feature = out_lyr.GetLayerDefn()
# 遍历原始的Shapefile文件给每个Geometry做Buffer操作
for feature in in_lyr:
geometry = feature.GetGeometryRef()
buffer = geometry.Buffer(bdistance).Buffer(-bdistance)
out_feature = ogr.Feature(def_feature)
out_feature.SetGeometry(buffer)
out_lyr.CreateFeature(out_feature)
out_feature = None
out_ds.FlushCache()
del in_ds, out_ds
def pol2line(polyfn, linefn):
"""
This function is used to make polygon convert to line
:param polyfn: the path of input, the shapefile of polygon
:param linefn: the path of output, the shapefile of line
:return:
"""
driver = ogr.GetDriverByName('ESRI Shapefile')
polyds = ogr.Open(polyfn, 0)
polyLayer = polyds.GetLayer()
spatialref = polyLayer.GetSpatialRef()
#创建输出文件
if os.path.exists(linefn):
driver.DeleteDataSource(linefn)
lineds =driver.CreateDataSource(linefn)
linelayer = lineds.CreateLayer(linefn, srs=spatialref, geom_type=ogr.wkbLineString)
featuredefn = linelayer.GetLayerDefn()
#获取ring到几何体
#geomline = ogr.Geometry(ogr.wkbGeometryCollection)
for feat in polyLayer:
geom = feat.GetGeometryRef()
ring = geom.GetGeometryRef(0)
#geomcoll.AddGeometry(ring)
outfeature = ogr.Feature(featuredefn)
outfeature.SetGeometry(ring)
linelayer.CreateFeature(outfeature)
outfeature = None
def ZonalStatisticsAsTable(ras_path, shp_path, stats_list=['majority']):
"""
please refer to https://blog.csdn.net/weixin_42990464/article/details/114652193
"""
start = time.time()
ras_driver = rasterio.open(ras_path)
array = ras_driver.read(1)
affine = ras_driver.transform
shp_driver = pd.read_file(shp_path)
zs = zonal_stats(shp_path, array, affine=affine, stats=stats_list)
driver = ogr.GetDriverByName('ESRI Shapefile')
layer_source = driver.Open(shp_path, 1)
lyr = layer_source.GetLayer()
defn = lyr.GetLayerDefn()
featureCount = defn.GetFieldCount()
exists_fields = []
for i in range(featureCount):
field = defn.GetFieldDefn(i)
field_name = field.GetNameRef()
exists_fields.append(field_name)
for ele in stats_list:
if ele in exists_fields:
pass
else:
# cls_name = ogr.FieldDefn(k, ogr.OFTString)
cls_name = ogr.FieldDefn(ele, ogr.OFTReal)
# cls_name.SetWidth(64)
lyr.CreateField(cls_name)
driver = None
driver = ogr.GetDriverByName('ESRI Shapefile')
layer_source = driver.Open(shp_path, 1)
lyr = layer_source.GetLayer()
defn = lyr.GetLayerDefn()
featureCount = defn.GetFieldCount()
count = 0
feature = lyr.GetNextFeature()
while feature is not None:
for i in range(featureCount):
field = defn.GetFieldDefn(i)
field_name = field.GetNameRef()
if field_name in stats_list:
feature.SetField(field_name, zs[count][field_name])
lyr.SetFeature(feature)
else:
pass
count += 1
feature = lyr.GetNextFeature()
end = time.time()
print((end - start) / 3600.0)
def compute_max_area(shpPath):
'''
compute max area among all features
:param shpPath: the absolute path of shapefile
:return: the max area
'''
max_area = 0
driver = ogr.GetDriverByName("ESRI Shapefile")
dataSource = driver.Open(shpPath, 1)
layer = dataSource.GetLayer()
new_field = ogr.FieldDefn("Area", ogr.OFTReal)
new_field.SetWidth(32)
new_field.SetPrecision(16) # 设置面积精度,小数点后16位
layer.CreateField(new_field)
for feature in layer:
geom = feature.GetGeometryRef()
area = geom.GetArea() # 计算面积
if area > max_area:
max_area = area
feature.SetField("Area", area) # 将面积添加到属性表中
layer.SetFeature(feature)
dataSource = None
return max_area
def extract_isolated_features(inShp, outshp, bdistance=0.008, temproot='./temp'):
"""
extract isolated features among all features(point, line, polygon)
:param inShp: the path of input shapefile
:param outshp: the path of output shapefile
:param bdistance: the distance of buffer
:param temproot: temporary file directory
:return: None
"""
mkdir(temproot)
fname = f'{temproot}/buffer.shp'
fname2 = f'{temproot}/buffer2.shp'
buffer(inShp, fname, bdistance=bdistance)
max_area = compute_max_area(fname)
MergeOneShp(fname, fname2)
multipoly2singlepoly(fname2, fname)
remove_big_feature(fname, fname2, max_area)
MergeOneShp(fname2, fname)
intersection(fname, inShp, outshp)
# remove temporary directory
if os.path.exists(temproot):
shutil.rmtree(temproot)
def simplify_shp(in_shp, out_shp, tolerance=0.0001):
"""
:param in_shp: the path of input shapefile
:param out_shp: the path of output shapefile
:return: Returns a simplified shapefile produced by the Douglas-Peucker
"""
gdf = pd.read_file(in_shp) #LINESTRING
gdf['geometry'] = simplify( gdf['geometry'], tolerance=tolerance)
gdf.to_file(out_shp, driver="ESRI Shapefile")
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62,
30053,
796,
503,
62,
306,
81,
13,
3855,
49925,
7469,
77,
3419,
198,
220,
220,
220,
1303,
16268,
223,
235,
43889,
228,
43889,
253,
34650,
233,
21410,
33383,
7753,
23877,
229,
20015,
114,
163,
119,
247,
162,
107,
237,
10310,
103,
10082,
15748,
161,
223,
248,
28632,
162,
241,
235,
43291,
198,
220,
220,
220,
1303,
1459,
62,
24592,
796,
7679,
58,
15,
4083,
2601,
505,
3419,
198,
220,
220,
220,
3601,
10786,
1169,
4129,
286,
7679,
25,
3256,
18896,
7,
29289,
4008,
198,
220,
220,
220,
611,
18896,
7,
29289,
8,
6624,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
628,
220,
220,
220,
329,
1312,
11,
3895,
287,
27056,
378,
7,
29289,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
22939,
796,
3895,
13,
3855,
10082,
15748,
8134,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
611,
1312,
6624,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1459,
62,
24592,
796,
22939,
13,
2601,
505,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
1459,
62,
24592,
796,
1459,
62,
24592,
13,
38176,
7,
469,
15748,
737,
2601,
505,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
611,
1312,
6624,
18896,
7,
29289,
8,
532,
352,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
503,
62,
30053,
796,
267,
2164,
13,
38816,
7,
4299,
62,
30053,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
503,
62,
30053,
13,
7248,
10082,
15748,
7,
14421,
62,
24592,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
503,
62,
306,
81,
13,
4965,
316,
36120,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
503,
62,
306,
81,
13,
16447,
38816,
7,
448,
62,
30053,
8,
628,
220,
220,
220,
1619,
1366,
7416,
11,
503,
62,
9310,
628,
198,
4299,
18540,
3366,
17,
29762,
35428,
7,
15414,
1477,
79,
11,
5072,
1477,
79,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
5021,
636,
284,
2060,
636,
198,
220,
220,
220,
1058,
17143,
5128,
1477,
79,
25,
262,
3108,
286,
5128,
5485,
7753,
198,
220,
220,
220,
1058,
17143,
5072,
1477,
79,
25,
262,
3108,
286,
5072,
5485,
7753,
198,
220,
220,
220,
1058,
7783,
25,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
308,
31748,
13,
11041,
3109,
11755,
3419,
198,
220,
220,
220,
4639,
796,
267,
2164,
13,
3855,
32103,
3886,
5376,
10786,
1546,
7112,
25959,
7753,
11537,
198,
220,
220,
220,
287,
62,
9310,
796,
4639,
13,
11505,
7,
15414,
1477,
79,
11,
657,
8,
198,
220,
220,
220,
287,
62,
306,
81,
796,
287,
62,
9310,
13,
3855,
49925,
3419,
198,
220,
220,
220,
611,
28686,
13,
6978,
13,
1069,
1023,
7,
22915,
1477,
79,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
4639,
13,
38727,
6601,
7416,
7,
22915,
1477,
79,
8,
198,
220,
220,
220,
503,
62,
9310,
796,
4639,
13,
16447,
6601,
7416,
7,
22915,
1477,
79,
8,
198,
220,
220,
220,
503,
62,
306,
81,
796,
503,
62,
9310,
13,
16447,
49925,
10786,
35428,
3256,
287,
62,
306,
81,
13,
3855,
4561,
34961,
8134,
22784,
4903,
296,
62,
4906,
28,
519,
81,
13,
86,
32812,
34220,
14520,
8,
198,
220,
220,
220,
329,
287,
62,
27594,
287,
287,
62,
306,
81,
25,
198,
220,
220,
220,
220,
220,
220,
220,
4903,
296,
796,
287,
62,
27594,
13,
3855,
10082,
15748,
8134,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
611,
4903,
296,
13,
3855,
10082,
15748,
5376,
3419,
6624,
705,
44,
16724,
4061,
3535,
56,
38,
1340,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
4903,
296,
62,
3911,
287,
4903,
296,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
751,
34220,
14520,
7,
469,
296,
62,
3911,
13,
43834,
2514,
54,
32812,
22784,
503,
62,
306,
81,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
751,
34220,
14520,
7,
469,
296,
13,
43834,
2514,
54,
32812,
22784,
503,
62,
306,
81,
8,
198,
220,
220,
220,
1619,
287,
62,
9310,
11,
503,
62,
9310,
628,
198,
198,
4299,
4781,
62,
14261,
62,
30053,
7,
15414,
2484,
79,
11,
5072,
2484,
79,
11,
1989,
62,
400,
10126,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
770,
2163,
318,
973,
284,
4781,
1263,
1989,
286,
3895,
422,
5485,
7753,
198,
220,
220,
220,
1058,
17143,
5128,
2484,
79,
25,
262,
3108,
286,
5128,
5485,
7753,
198,
220,
220,
220,
1058,
17143,
5072,
2484,
79,
25,
262,
3108,
286,
5072,
5485,
7753,
198,
220,
220,
220,
1058,
17143,
1989,
62,
400,
411,
727,
25,
262,
11387,
286,
1989,
198,
220,
220,
220,
1058,
7783,
25,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
4639,
796,
267,
2164,
13,
3855,
32103,
3886,
5376,
7203,
1546,
7112,
25959,
7753,
4943,
198,
220,
220,
220,
1366,
7416,
796,
4639,
13,
11505,
7,
15414,
2484,
79,
11,
352,
8,
198,
220,
220,
220,
7679,
796,
1366,
7416,
13,
3855,
49925,
3419,
198,
220,
220,
220,
649,
62,
3245,
796,
267,
2164,
13,
15878,
7469,
77,
7203,
30547,
1600,
267,
2164,
13,
19238,
5446,
2287,
8,
198,
220,
220,
220,
649,
62,
3245,
13,
7248,
30916,
7,
2624,
8,
198,
220,
220,
220,
649,
62,
3245,
13,
7248,
6719,
16005,
7,
1433,
8,
220,
1303,
5525,
106,
122,
163,
121,
106,
165,
251,
95,
163,
100,
107,
163,
39333,
41753,
99,
11,
22887,
237,
46763,
108,
163,
224,
117,
28938,
236,
1433,
19526,
235,
198,
220,
220,
220,
7679,
13,
16447,
15878,
7,
3605,
62,
3245,
8,
628,
220,
220,
220,
1303,
10545,
244,
108,
161,
119,
118,
6601,
7416,
171,
120,
234,
49925,
198,
220,
220,
220,
503,
62,
9310,
796,
4639,
13,
16447,
6601,
7416,
7,
22915,
2484,
79,
8,
198,
220,
220,
220,
503,
62,
306,
81,
796,
503,
62,
9310,
13,
16447,
49925,
7,
22915,
2484,
79,
11,
7679,
13,
3855,
4561,
34961,
8134,
22784,
267,
2164,
13,
86,
32812,
34220,
14520,
8,
198,
220,
220,
220,
825,
62,
30053,
796,
503,
62,
306,
81,
13,
3855,
49925,
7469,
77,
3419,
628,
220,
220,
220,
329,
3895,
287,
7679,
25,
198,
220,
220,
220,
220,
220,
220,
220,
4903,
296,
796,
3895,
13,
3855,
10082,
15748,
8134,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
1989,
796,
4903,
296,
13,
3855,
30547,
3419,
220,
1303,
5525,
106,
94,
163,
106,
245,
165,
251,
95,
163,
100,
107,
198,
220,
220,
220,
220,
220,
220,
220,
611,
1989,
1875,
1989,
62,
400,
10126,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2555,
198,
220,
220,
220,
220,
220,
220,
220,
3895,
13,
7248,
15878,
7203,
30547,
1600,
1989,
8,
220,
1303,
10263,
108,
228,
165,
251,
95,
163,
100,
107,
162,
115,
119,
27950,
254,
26344,
108,
161,
109,
252,
45250,
100,
26193,
101,
40792,
198,
220,
220,
220,
220,
220,
220,
220,
7679,
13,
7248,
38816,
7,
30053,
8,
198,
220,
220,
220,
220,
220,
220,
220,
503,
62,
30053,
796,
267,
2164,
13,
38816,
7,
4299,
62,
30053,
8,
198,
220,
220,
220,
220,
220,
220,
220,
503,
62,
30053,
13,
7248,
10082,
15748,
7,
469,
296,
8,
198,
220,
220,
220,
220,
220,
220,
220,
503,
62,
306,
81,
13,
16447,
38816,
7,
448,
62,
30053,
8,
198,
220,
220,
220,
220,
220,
220,
220,
503,
62,
30053,
796,
6045,
628,
220,
220,
220,
503,
62,
9310,
13,
7414,
1530,
30562,
3419,
198,
220,
220,
220,
1619,
1366,
7416,
11,
503,
62,
9310,
628,
198,
4299,
4781,
62,
17470,
62,
30053,
7,
15414,
2484,
79,
11,
5072,
2484,
79,
11,
1989,
62,
400,
10126,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
770,
2163,
318,
973,
284,
4781,
1402,
1989,
286,
3895,
422,
5485,
7753,
198,
220,
220,
220,
1058,
17143,
5128,
2484,
79,
25,
262,
3108,
286,
5128,
5485,
7753,
198,
220,
220,
220,
1058,
17143,
5072,
2484,
79,
25,
262,
3108,
286,
5072,
5485,
7753,
198,
220,
220,
220,
1058,
17143,
1989,
62,
400,
411,
727,
25,
262,
11387,
286,
1989,
198,
220,
220,
220,
1058,
7783,
25,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
4639,
796,
267,
2164,
13,
3855,
32103,
3886,
5376,
7203,
1546,
7112,
25959,
7753,
4943,
198,
220,
220,
220,
1366,
7416,
796,
4639,
13,
11505,
7,
15414,
2484,
79,
11,
352,
8,
198,
220,
220,
220,
7679,
796,
1366,
7416,
13,
3855,
49925,
3419,
198,
220,
220,
220,
649,
62,
3245,
796,
267,
2164,
13,
15878,
7469,
77,
7203,
30547,
1600,
267,
2164,
13,
19238,
5446,
2287,
8,
198,
220,
220,
220,
649,
62,
3245,
13,
7248,
30916,
7,
2624,
8,
198,
220,
220,
220,
649,
62,
3245,
13,
7248,
6719,
16005,
7,
1433,
8,
220,
1303,
5525,
106,
122,
163,
121,
106,
165,
251,
95,
163,
100,
107,
163,
39333,
41753,
99,
11,
22887,
237,
46763,
108,
163,
224,
117,
28938,
236,
1433,
19526,
235,
198,
220,
220,
220,
7679,
13,
16447,
15878,
7,
3605,
62,
3245,
8,
628,
220,
220,
220,
1303,
10545,
244,
108,
161,
119,
118,
6601,
7416,
171,
120,
234,
49925,
198,
220,
220,
220,
503,
62,
9310,
796,
4639,
13,
16447,
6601,
7416,
7,
22915,
2484,
79,
8,
198,
220,
220,
220,
503,
62,
306,
81,
796,
503,
62,
9310,
13,
16447,
49925,
7,
22915,
2484,
79,
11,
7679,
13,
3855,
4561,
34961,
8134,
22784,
267,
2164,
13,
86,
32812,
34220,
14520,
8,
198,
220,
220,
220,
825,
62,
30053,
796,
503,
62,
306,
81,
13,
3855,
49925,
7469,
77,
3419,
628,
220,
220,
220,
329,
3895,
287,
7679,
25,
198,
220,
220,
220,
220,
220,
220,
220,
4903,
296,
796,
3895,
13,
3855,
10082,
15748,
8134,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
1989,
796,
4903,
296,
13,
3855,
30547,
3419,
220,
1303,
5525,
106,
94,
163,
106,
245,
165,
251,
95,
163,
100,
107,
198,
220,
220,
220,
220,
220,
220,
220,
611,
1989,
1279,
1989,
62,
400,
10126,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2555,
198,
220,
220,
220,
220,
220,
220,
220,
3895,
13,
7248,
15878,
7203,
30547,
1600,
1989,
8,
220,
1303,
10263,
108,
228,
165,
251,
95,
163,
100,
107,
162,
115,
119,
27950,
254,
26344,
108,
161,
109,
252,
45250,
100,
26193,
101,
40792,
198,
220,
220,
220,
220,
220,
220,
220,
7679,
13,
7248,
38816,
7,
30053,
8,
198,
220,
220,
220,
220,
220,
220,
220,
503,
62,
30053,
796,
267,
2164,
13,
38816,
7,
4299,
62,
30053,
8,
198,
220,
220,
220,
220,
220,
220,
220,
503,
62,
30053,
13,
7248,
10082,
15748,
7,
469,
296,
8,
198,
220,
220,
220,
220,
220,
220,
220,
503,
62,
306,
81,
13,
16447,
38816,
7,
448,
62,
30053,
8,
198,
220,
220,
220,
220,
220,
220,
220,
503,
62,
30053,
796,
6045,
628,
220,
220,
220,
503,
62,
9310,
13,
7414,
1530,
30562,
3419,
198,
220,
220,
220,
1619,
1366,
7416,
11,
503,
62,
9310,
628,
198,
4299,
11876,
7,
259,
2484,
79,
11,
503,
2484,
79,
11,
275,
30246,
28,
15,
13,
2999,
2599,
198,
220,
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628
] | 2.211545 | 5,838 |
from memoized import memoized
from custom.inddex import filters
from custom.inddex.ucr.data_providers.nutrient_intakes_data import (
NutrientIntakesByFoodData,
NutrientIntakesByRespondentData,
)
from custom.inddex.utils import MultiTabularReport
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] | 3.047619 | 84 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# This file is part of the minifold project.
# https://github.com/nokia/minifold
__author__ = "Marc-Olivier Buob"
__maintainer__ = "Marc-Olivier Buob"
__email__ = "[email protected]"
__copyright__ = "Copyright (C) 2018, Nokia"
__license__ = "BSD-3"
def cast_none(s :str):
"""
Cast a string into None if possible.
Args:
s: The input str.
Raises:
ValueError: if the cast cannot be achieved.
Returns:
None if successful.
"""
if s is None:
return None
elif isinstance(s, str) and s.lower() == "none":
return None
raise ValueError("Invalid literal for cast_none(): %s" % s)
def cast_bool(s :str) -> bool:
"""
Cast a string into a bool if possible.
Args:
s: The input str.
Raises:
ValueError: if the cast cannot be achieved.
Returns:
The boolean corresponding to s if successful.
"""
if isinstance(s, bool):
return s
elif isinstance(s, str):
l = s.lower()
if l == "true":
return True
elif l == "false":
return False
raise ValueError("Invalid literal for cast_bool(): %s" % s)
def lexical_cast(s :str, cast):
"""
Cast a string according to an operator.
Args:
cast: The cast operator.
s: The string to be casted.
Raises:
ValueError: if the cast cannot be achieved.
Returns:
The corresponding value.
"""
return cast(s)
def lexical_casts(s :str, cast_operators = None):
"""
Cast a string according to several cast operators.
Args:
s: The string to be casted.
cast_operators: A list of cast operators. Operators must be ordered
from the less strict to the more strict (e.g. int before float).
Returns:
The original string if no cast worked, the corresponding casted value
otherwise.
"""
if cast_operators is None:
cast_operators = [cast_none, cast_bool, int, float]
for cast in cast_operators:
try:
ret = lexical_cast(s, cast)
return ret
except ValueError:
pass
return s
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] | 2.330553 | 959 |
import streamlit as st
import pandas as pd
import numpy as np
#Pour la modélisation
from sklearn import preprocessing
from sklearn.preprocessing import PolynomialFeatures
from sklearn.model_selection import train_test_split , cross_val_score, GridSearchCV
from sklearn.metrics import mean_squared_error, f1_score
from sklearn import linear_model
from sklearn.linear_model import RidgeCV , LassoCV, lasso_path, Ridge
from imblearn.metrics import classification_report_imbalanced, geometric_mean_score
import xgboost
from xgboost import XGBRegressor
#Pour les graphs
import matplotlib.pyplot as plt
import seaborn as sns
from bokeh.io import output_file, show
from bokeh.plotting import figure
from numpy import histogram, linspace
from scipy.stats.kde import gaussian_kde
from bokeh.models import HoverTool, LinearAxis, Range1d, ColumnDataSource
from bokeh.models.widgets import Panel, Tabs
from bokeh.models.annotations import BoxAnnotation
#Pour l'interprétabilité'
import streamlit.components.v1 as components
import random
import shap
shap.initjs()
import eli5
from skater.core.explanations import Interpretation
interpreter = Interpretation()
from skater.data import DataManager
from skater.model import InMemoryModel , DeployedModel
title = "Modélisation"
sidebar_name = "Modélisation"
@st.cache
@st.cache()
@st.cache()
@st.cache(hash_funcs={XGBRegressor: id})
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25,
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] | 3.150342 | 439 |
from django.core.paginator import Paginator, PageNotAnInteger, InvalidPage, EmptyPage
from django.http import HttpResponse
from django.shortcuts import render
from .models import Tweets
# Create your views here.
| [
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] | 3.279412 | 68 |
from spook.base import SpookBase
from spook.lin_solve import SpookLinSolve
from spook.quad_program import SpookPosL1,SpookPosL2,SpookL1
import spook.utils as utils
# from spook.vmi_special import PhotonFreqResVMI | [
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] | 2.789474 | 76 |
from tkinter import Frame
from .mixins import WidgetMixin, ContainerMixin
from .tkmixins import ScheduleMixin, DestroyMixin, FocusMixin, DisplayMixin, ColorMixin, ReprMixin
from . import utilities as utils
| [
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] | 3.377049 | 61 |
#
# Copyright (c) 2019 Idiap Research Institute, http://www.idiap.ch/
# Written by Angelos Katharopoulos <[email protected]>
#
"""Provide utilities for training attention sampling models."""
from keras.utils import Sequence
import numpy as np
class Batcher(Sequence):
"""Assemble a sequence of things into a sequence of batches."""
class DataTransform(Sequence):
"""Apply a transform to the inputs before passing them to keras."""
class LambdaTransform(DataTransform):
"""Apply the data transformation defined by the passed in transform
function."""
def compose_sequences(sequence, sequences):
"""Compose a sequence with other sequences.
Example
sequence = compose_sequences(Dataset(), [
(Batcher, 32),
(LambdaTransform, lambda x: x.expand_dims(-1))
])
"""
for s in sequences:
sequence = s[0](sequence, *s[1:])
return sequence
| [
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220,
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220,
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198,
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220,
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329,
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220,
220,
8379,
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11,
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82,
58,
16,
25,
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198,
220,
220,
220,
1441,
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198
] | 2.977707 | 314 |
import frappe | [
11748,
5306,
27768
] | 4.333333 | 3 |
from couchdbkit import ResourceNotFound
from dimagi.utils.couch.database import get_db
from dimagi.utils.couch.undo import DELETED_SUFFIX
from django.core.urlresolvers import reverse
from django.utils.translation import ugettext as _
from dimagi.ext.jsonobject import *
from corehq.apps.users.models import CouchUser
from corehq.apps.users.util import raw_username
from couchforms import models as couchforms_models
def get_doc_info(doc, domain_hint=None, cache=None):
"""
cache is just a dictionary that you can keep passing in to speed up info
retrieval.
"""
domain = doc.get('domain') or domain_hint
doc_type = doc.get('doc_type')
doc_id = doc.get('_id')
generic_delete = doc_type.endswith(DELETED_SUFFIX)
assert doc.get('domain') == domain or domain in doc.get('domains', ())
if cache and doc_id in cache:
return cache[doc_id]
if has_doc_type(doc_type, 'Application') or has_doc_type(doc_type, 'RemoteApp'):
if doc.get('copy_of'):
doc_info = DocInfo(
display=u'%s (#%s)' % (doc['name'], doc['version']),
type_display=_('Application Build'),
link=reverse(
'corehq.apps.app_manager.views.download_index',
args=[domain, doc_id],
),
is_deleted=generic_delete,
)
else:
doc_info = DocInfo(
display=doc['name'],
type_display=_('Application'),
link=reverse(
'corehq.apps.app_manager.views.view_app',
args=[domain, doc_id],
),
is_deleted=generic_delete,
)
elif has_doc_type(doc_type, 'CommCareCaseGroup'):
from corehq.apps.data_interfaces.views import CaseGroupCaseManagementView
doc_info = DocInfo(
type_display=_('Case Group'),
display=doc['name'],
link=reverse(
CaseGroupCaseManagementView.urlname,
args=[domain, doc_id],
),
is_deleted=generic_delete,
)
elif has_doc_type(doc_type, 'CommCareCase'):
doc_info = DocInfo(
display=doc['name'],
type_display=_('Case'),
link=reverse(
'case_details',
args=[domain, doc_id],
),
is_deleted=generic_delete,
)
elif any([has_doc_type(doc_type, d) for d in couchforms_models.doc_types().keys()]):
doc_info = DocInfo(
type_display=_('Form'),
link=reverse(
'render_form_data',
args=[domain, doc_id],
),
is_deleted=generic_delete,
)
elif doc_type in ('CommCareUser',):
doc_info = DocInfo(
display=raw_username(doc['username']),
type_display=_('Mobile Worker'),
link=reverse(
'edit_commcare_user',
args=[domain, doc_id],
),
is_deleted=doc.get('base_doc', '').endswith(DELETED_SUFFIX),
)
elif doc_type in ('WebUser',):
doc_info = DocInfo(
type_display=_('Web User'),
display=doc['username'],
link=reverse(
'user_account',
args=[domain, doc_id],
),
is_deleted=doc.get('base_doc', '').endswith(DELETED_SUFFIX),
)
elif has_doc_type(doc_type, 'Group'):
from corehq.apps.users.views.mobile import EditGroupMembersView
doc_info = DocInfo(
type_display=_('Group'),
display=doc['name'],
link=reverse(
EditGroupMembersView.urlname,
args=[domain, doc_id],
),
is_deleted=generic_delete,
)
elif has_doc_type(doc_type, 'Domain'):
if doc['is_snapshot'] and doc['published']:
urlname = 'project_info'
else:
urlname = 'domain_basic_info'
doc_info = DocInfo(
type_display=_('Domain'),
display=doc['name'],
link=reverse(
urlname,
kwargs={'domain' : doc['name']}
),
is_deleted=generic_delete,
)
elif has_doc_type(doc_type, 'Location'):
from corehq.apps.locations.views import EditLocationView
doc_info = DocInfo(
type_display=_('Location'),
display=doc['name'],
link=reverse(
EditLocationView.urlname,
args=[domain, doc_id],
),
is_deleted=generic_delete,
)
else:
doc_info = DocInfo(
is_deleted=generic_delete,
)
doc_info.id = doc_id
doc_info.domain = domain
doc_info.type = doc_type
if cache:
cache[doc_id] = doc_info
return doc_info
def get_object_info(obj, cache=None):
"""
This function is intended to behave just like get_doc_info, only
you call it with objects other than Couch docs (such as objects
that use the Django ORM).
"""
class_name = obj.__class__.__name__
cache_key = '%s-%s' % (class_name, obj.pk)
if cache and cache_key in cache:
return cache[cache_key]
from corehq.apps.locations.models import SQLLocation
if isinstance(obj, SQLLocation):
from corehq.apps.locations.views import EditLocationView
doc_info = DocInfo(
type_display=_('Location'),
display=obj.name,
link=reverse(
EditLocationView.urlname,
args=[obj.domain, obj.location_id],
),
is_deleted=False,
)
else:
doc_info = DocInfo(
is_deleted=False,
)
doc_info.id = str(obj.pk)
doc_info.domain = obj.domain if hasattr(obj, 'domain') else None
doc_info.type = class_name
if cache:
cache[cache_key] = doc_info
return doc_info
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] | 1.980237 | 3,036 |
import argparse
import sys
# import assemble
import disassemble
import sutil
parser = argparse.ArgumentParser(
description='The Lida assembler service for Lua',
allow_abbrev=True
)
# Argument groups
action = parser.add_mutually_exclusive_group()
g_assembler = parser.add_argument_group('assembler')
g_disassembler = parser.add_argument_group('disassembler')
output = parser.add_argument_group('output')
where = output.add_mutually_exclusive_group()
action.add_argument(
'-a', '--assemble',
action='store_true',
help='assemble a file into bytecode'
)
action.add_argument(
'-d', '--disassemble',
action='store_true',
help='disassemble a file into listing'
)
parser.add_argument(
'-v', '--verbose',
action='store_true',
help='show debug information'
)
# Disassembler
g_disassembler.add_argument(
'-C', '--comments',
action='store_true',
help='show comments on complex instructions'
)
g_disassembler.add_argument(
'-L', '--nolines',
action='store_true',
help='hide line info behind instructions'
)
g_disassembler.add_argument(
'-I', '--inlconsts',
action='store_true',
help='inlines use of constants in instructions'
)
g_disassembler.add_argument(
'-J', '--smartjumps',
action='store_true',
help='simplify labels for unoptimized JMPs'
)
# Output stuff
where.add_argument(
'-o', '--output',
type=str,
metavar='file',
default='lida.out',
help='set the output file for assembly and disassembly (default: lida.out)'
)
where.add_argument(
'-e', '--echo',
action='store_true',
help='outputs to stdout instead of a file'
)
output.add_argument(
'files',
type=str,
nargs='+',
help='file names to load'
)
arg_list = parser.parse_args()
if arg_list.assemble:
raise NotImplementedError("Assembler is not yet implemented")
# process = assemble.process_query
elif arg_list.disassemble:
process = disassemble.process_query
else:
process = None # silence warning
parser.error('expected a mode')
output = get_out_file(arg_list)
timer = sutil.LightTimer()
process(output, arg_list)
if output != sys.stdout:
output.close()
if arg_list.verbose:
print(f"Finished in {timer.pretty()}")
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7,
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1,
18467,
1348,
287,
1391,
45016,
13,
37784,
3419,
92,
4943,
198
] | 2.823138 | 752 |
# Copyright 2021 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
from __future__ import annotations
from textwrap import dedent
import pytest
from pants.backend.go.target_types import GoModTarget, GoPackageTarget
from pants.backend.go.util_rules import go_mod, sdk
from pants.backend.go.util_rules.go_mod import (
GoModInfo,
GoModInfoRequest,
OwningGoMod,
OwningGoModRequest,
)
from pants.build_graph.address import Address
from pants.engine.rules import QueryRule
from pants.testutil.rule_runner import RuleRunner
@pytest.fixture
| [
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] | 3.217617 | 193 |
# @Rexhino_Kovaci
# This is an extra exercises that I challenged myself with on Google Kickstart 2020 during the online sessions
# This algorithm takes user input and compare the left and right subtree
# this would allow us to maintain a sorted list of numbers
# this program would check if the tree is balanced between the height of right and left subtree is 1 or 0
# This would print all the properties of the nodes list, order, size, height, properties of our Balanced Search Tree
| [
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] | 4.304348 | 115 |
# coding: utf8
"""
This file contains a set of functional tests designed to check the correct execution of the pipeline and the
different functions available in Clinica
"""
import warnings
from os import pardir
from test.nonregression.testing_tools import *
# Determine location for working_directory
warnings.filterwarnings("ignore")
| [
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] | 4.034884 | 86 |
from python_qt_binding import QtCore
from python_qt_binding import QtGui
from python_qt_binding import QtWidgets
from ..core import myutils
#def config_removed(self, lpath):
| [
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] | 3.118644 | 59 |
base_path_source = './'
base_path_target = '../NC2016/'
files = ['concatenated_en2de_dev_de.txt', 'concatenated_en2de_dev_en.txt', 'concatenated_en2de_test_de.txt', 'concatenated_en2de_test_en.txt', 'concatenated_en2de_train_de.txt', 'concatenated_en2de_train_en.txt']
for file_path in files:
source_path = base_path_source + file_path
target_path = base_path_target + file_path
fr = open(source_path, 'r')
items = [item for item in fr.read().split('<d>\n') if item]
fr.close()
fw = open(target_path, 'w')
fw.write(''.join(items))
fw.close()
| [
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220,
220,
220,
277,
86,
13,
19836,
3419,
198,
220,
220,
220,
220
] | 2.243243 | 259 |
import sys
import os
import re
if __name__ == "__main__":
sys.exit(main()) | [
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"""Library function to build Integrate and fire model with square-pulse
spike, as a hybrid system.
Robert Clewley, March 2005.
"""
from __future__ import print_function
from PyDSTool import *
from time import clock
# ----------------------------------------------------------------
# ----------------------------------------------------------------
if __name__=='__main__':
# need the __main__ to use above functions as imports for other
# scripts without running this part
print('-------- IF model test 1')
par_args_linear = {'Iapp': 1.3, 'gl': 0.1, 'vl': -67,
'threshval': -65, 'C': 1}
par_args_spike = {'splen': 0.75}
IFmodel = makeIFneuron('IF_fit', par_args_linear, par_args_spike)
icdict = {'v': -80, 'excited': 0}
start = clock()
print('Computing trajectory...')
IFmodel.compute(trajname='onespike',
tdata=[0, 60],
ics=icdict,
verboselevel=0)
print('\n... finished in %.3f seconds.\n' % (clock()-start))
IFmodel.set(pars={'Iapp': 1.0, 'threshval': -60})
print('Recomputing trajectory with new params...')
IFmodel.compute(trajname='twospike',
tdata=[0, 60],
ics=icdict)
print('Preparing plot')
plotData = IFmodel.sample('onespike', dt=0.05)
plotData2 = IFmodel.sample('twospike', ['v', 'testaux'], 0.05)
plt.ylabel('v, testaux')
plt.xlabel('t')
vline = plt.plot(plotData['t'], plotData['v'])
vline2 = plt.plot(plotData2['t'], plotData2['v'])
aline = plt.plot(plotData['t'], plotData['testaux'])
print("\nLast point of hybrid trajectory: ")
print("IFmodel.getEndPoint('onespike') -->\n", end='')
print(IFmodel.getEndPoint('onespike'))
print("\nFirst point of hybrid trajectory: ")
print("IFmodel.getEndPoint('onespike', 0) -->\n", end='')
print(IFmodel.getEndPoint('onespike', 0))
print("Testing IF hybrid model as mapping ...")
num_parts = len(IFmodel.getTrajTimePartitions('twospike'))
#eventvals = IFmodel('onespike', range(0, num_parts+1), asmap=True)
eventvals = IFmodel('twospike', list(range(0, num_parts+1)), asmap=True)
for i in range(0,num_parts+1):
print("(v, t) at event(%i) = (%.4f, %.4f)" % (i, eventvals(i)('v'),
eventvals(i)('t')))
print("\nAlternative access to explicit event info using " + \
"getTrajEvents(trajname) method:\n")
evs = IFmodel.getTrajEvents('twospike')
evtimes = IFmodel.getTrajEventTimes('onespike')
print(evs)
assert len(evs['threshold']) == 2, "Problem with hybrid events"
assert len(evtimes['threshold']) == 4, "Problem with hybrid events"
assert allclose(evtimes['threshold'][3], 54.009, 1e-3), \
"Problem with hybrid events"
assert allclose(evs['threshold'][1]['v'], -60, 1e-3), \
"Problem with hybrid events"
print("\nDepending on your platform and matplotlib configuration you may need")
print(" to execute the plt.show() command to see the plots")
# plt.show()
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] | 2.376987 | 1,321 |
# -*- coding: utf-8 -*-
"""
Eve Demo
~~~~~~~~
A demostration of a simple API powered by Eve REST API.
The live demo is available at eve-demo.herokuapp.com. Please keep in mind
that the it is running on Heroku's free tier using a free MongoHQ
sandbox, which means that the first request to the service will probably
be slow. The database gets a reset every now and then.
:copyright: (c) 2016 by Nicola Iarocci.
:license: BSD, see LICENSE for more details.
"""
from eve import Eve
app = Eve()
@app.route('/hello')
if __name__ == '__main__':
app.run()
| [
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] | 2.902913 | 206 |
# -*- coding: utf-8 -*-
#
# This file is part of Invenio.
# Copyright (C) 2016-2019 CERN.
#
# Invenio is free software; you can redistribute it and/or modify it
# under the terms of the MIT License; see LICENSE file for more details.
"""Jupyter Notebook previewer."""
from __future__ import absolute_import, unicode_literals
import nbformat
from flask import render_template
from nbconvert import HTMLExporter
def render(file):
"""Generate the result HTML."""
with file.open() as fp:
content = fp.read()
notebook = nbformat.reads(content.decode('utf-8'), as_version=4)
html_exporter = HTMLExporter()
html_exporter.template_file = 'basic'
(body, resources) = html_exporter.from_notebook_node(notebook)
return body, resources
def can_preview(file):
"""Determine if file can be previewed."""
return file.is_local() and file.has_extensions('.ipynb')
def preview(file):
"""Render the IPython Notebook."""
body, resources = render(file)
default_jupyter_nb_style = resources['inlining']['css'][1]
return render_template(
'invenio_previewer/ipynb.html',
file=file,
content=body,
inline_style=default_jupyter_nb_style
)
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198,
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220,
220,
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198
] | 2.636364 | 462 |
#! /usr/bin/python3
import os
import math
from scipy.interpolate import interp1d
N = 100 # number of points of approximation
f1 = 'data/exp/M186/p_p0.txt' # data for curve №1
f2 = 'data/theory/M186/upper.xy' # data for curve №2
# x must be first column
n1 = 2 # column for curve №1
n2 = 2 # column for curve №2
scale1x = 1 # scale for curve №1 along the x axis
scale2x = 1 # scale for curve №2 along the x axis
scale1y = 638331.089701564 # scale for curve №1 along the y axis 638331.089701564
scale2y = 1 # scale for curve №2 along the y axis
# function of extract of data
# find X1 and X2
_f1 = open(f1, 'r')
_f2 = open(f2, 'r')
for line in _f1:
words = line.split()
if words[0] == '#':
_f1.readline()
else:
x11 = [float(w) for w in _f1.readline().split()]
x12 = [float(w) for w in _f1.readlines()[-1].split()]
for line in _f2:
words = line.split()
if words[0] == '#':
_f2.readline()
else:
x21 = [float(w) for w in _f2.readline().split()]
x22 = [float(w) for w in _f2.readlines()[-1].split()]
_f1.close()
_f2.close()
if x11[0]*scale1x > x21[0]*scale2x:
x1 = x11[0]*scale1x
else:
x1 = x21[0]*scale2x
if x12[0]*scale1x > x22[0]*scale2x:
x2 = x22[0]*scale2x
else:
x2 = x12[0]*scale1x
i = 0
lx = []
for _ in range (N):
if i+1 < N:
lx.append(x1+(x2-x1)/(N-1)*i)
else:
lx.append(x2)
i+=1
_lx1 = extract_data(f1, 0, scale1x)
_lx2 = extract_data(f2, 0, scale2x)
_ly1 = extract_data(f1, n1-1, scale1y)
_ly2 = extract_data(f2, n2-1, scale2y)
ly1_f = interp1d(_lx1, _ly1)
#ly2_f = interp1d(_lx2, _ly2)
ly1 = ly1_f(lx)
#ly2 = ly2_f(lx)
z = 0
ly2 = []
for _ in range (N):
if z < N/2:
ly2.append(_ly2[0])
else:
ly2.append(_ly2[3])
z+=1
curv = open('Curves.txt', 'w')
j = 0
for _ in range (N):
curv.write(str(lx[j]) + '\t')
curv.write(str(ly1[j]) + '\t')
curv.write(str(ly2[j]) + '\n')
j+=1
curv.close()
k = 0
dy = []
for _ in range (N):
dy_ = (ly1[k]-ly2[k])
dy.append(dy_)
k+=1
dys = sum(dy)/N
l = 0
sig_i = []
otn_i = []
for _ in range (N):
sig_i_=(dy[l]-dys)**2
sig_i.append(sig_i_)
otn_i_ = math.fabs(dy[l]/ly1[l])*100
otn_i.append(otn_i_)
l+=1
sigma = (sum(sig_i)/N)**0.5
otn = sum(otn_i)/N
print('Standard deviation: ', sigma)
print('Medium relative precision: ', otn, '%')
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] | 1.854701 | 1,287 |
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import abc
import netaddr
from oslo_db import exception
from oslo_db.sqlalchemy import test_base
from oslo_utils import timeutils
from oslo_utils import uuidutils
import six
import sqlalchemy as sa
from neutron import context
from neutron.db import sqlalchemytypes
from neutron.tests import tools
@six.add_metaclass(abc.ABCMeta)
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] | 3.37037 | 270 |
# coding=UTF-8
from scout.server.app import create_app
from scout.server.extensions import cloud_tracks
def test_align_handler_public_tracks(igv_test_tracks):
"""Test The class creating cloud tracks with public tracks"""
# GIVEN app config settings with a custom cloud public track
config = dict(CLOUD_IGV_TRACKS=igv_test_tracks)
# THEN the initialized app should create a cloud_tracks extension
app = create_app(config=config)
# Contanining the public track
assert cloud_tracks.public_tracks["37"][0]["name"] == "Test public track"
| [
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8,
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1303,
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272,
3191,
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62,
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13,
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62,
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1,
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15,
7131,
1,
3672,
8973,
6624,
366,
14402,
1171,
2610,
1,
198
] | 3.204545 | 176 |
from regene.expression import Expression, Quantifier
| [
6738,
842,
1734,
13,
38011,
1330,
41986,
11,
16972,
7483,
628
] | 4.909091 | 11 |
#####################################################################################
#
# Copyright (c) Microsoft Corporation. All rights reserved.
#
# This source code is subject to terms and conditions of the Microsoft Public License. A
# copy of the license can be found in the License.html file at the root of this distribution. If
# you cannot locate the Microsoft Public License, please send an email to
# [email protected]. By using this source code in any fashion, you are agreeing to be bound
# by the terms of the Microsoft Public License.
#
# You must not remove this notice, or any other, from this software.
#
#
#####################################################################################
'''
Delegate
* How the delegate is defined?
- Different signatures (return type, argument list, params,
params dictionary, ref/out args)
- Generic delegate type
* Instantiation
- With static/Instance (variable or literal) CLR method on value type/reference
type,
* When Type.Method has overloads
* No match or more than one matching method found.
* Type|Instance (dot) StaticMethod|InstanceMethod
- Another delegate
* Type compatible, or not
* Pri 2: it might be interesting to support some "light-weight coercion" here
where compatible delegates cast w/o new code gen. It's a feature, but just
adding some minimal tests of coercible delegates would be interesting. For
example a delegate object foo(subclass a) could have a otherclass bar(baseclass a)
cast to it - such conversions should be implicit.
- Others:
* a type, indexer, field, operator, property, NULL
* interface.Method, ...?
* Language function/methods
- Possible need for type conversion
* Static type should not be in the signature
- C# reference: http://msdn2.microsoft.com/en-us/library/aa691347(VS.71).aspx
* Operations on delegate
- +, +=, -, -=
* Add the same method multiple times
* Removing once - the last occurrence is the one actually removed
* Removing the same method multiple times
* Impossible removal is benign
* Becomes empty invocation list after removing.
- invocation
* call expression, __call__, "Invoke"
- other wild operations
Event
* Where it is defined
- Interface
* How it is defined
- Add only, remove only, different accessibility / modifiers
- Static or instance
- Explicit event from interface
* Operations on Type|object (dot) (Static|Intance) Event
- +=, -=, =, Add/Remove (?)
* The choices of the right side:
* delegate, method, others
* compatible, not compatible
* (python) __add__, __iadd__, __sub__, __isub__ direct calls
- call operator, explicit "invoke"?
- Other operations:
* Use it as the right-hand operand somewhere else?
'''
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286,
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25,
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9,
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11,
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198,
9,
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11,
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11670,
220,
220,
198,
220,
220,
220,
1635,
357,
29412,
8,
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2860,
834,
11,
11593,
72,
2860,
834,
11,
11593,
7266,
834,
11,
11593,
271,
549,
834,
1277,
3848,
198,
220,
532,
869,
10088,
11,
7952,
366,
37669,
13984,
198,
220,
532,
3819,
4560,
25,
198,
220,
220,
220,
1635,
5765,
340,
355,
262,
826,
12,
4993,
1515,
392,
7382,
2073,
30,
198,
7061,
6,
198
] | 3.729223 | 746 |
# Generated by Django 2.2.6 on 2019-10-21 11:10
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
import django.utils.timezone
| [
2,
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] | 3.04918 | 61 |
"""Utilities
Azure IoT Edge utilities.
functions:
is_edge: depends is edge or not
inference_module_url: get inference_module_url
"""
import logging
from azure.iot.device import IoTHubModuleClient
logger = logging.getLogger(__name__)
def is_edge() -> bool:
"""is_edge.
Args:
Returns:
is_edge -> bool
"""
try:
IoTHubModuleClient.create_from_edge_environment()
return True
except:
return False
def inference_module_url() -> str:
"""inference_module_url.
Args:
Returns:
str: inference_module_url
"""
if is_edge():
return "172.18.0.1:5000"
return "localhost:5000"
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220,
220,
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25,
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62,
21412,
62,
6371,
198,
220,
220,
220,
37227,
628,
220,
220,
220,
611,
318,
62,
14907,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
366,
23628,
13,
1507,
13,
15,
13,
16,
25,
27641,
1,
198,
220,
220,
220,
1441,
366,
36750,
25,
27641,
1,
198
] | 2.400709 | 282 |
import smart_imports
smart_imports.all()
| [
198,
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4451,
62,
320,
3742,
198,
198,
27004,
62,
320,
3742,
13,
439,
3419,
628
] | 2.75 | 16 |
"""
Writes strings containing the rate parameters
"""
# Functions to write the parameters in the correct format
def troe(reaction, high_params, low_params, troe_params, colliders=(), ea_units='kcal/mol'):
""" Write the string containing the Lindemann fitting parameters
formatted for ChemKin input files.
:param reaction: ChemKin formatted string with chemical equation
:type reaction: str
:param high_params: Arrhenius Fitting Parameters used for high-P
:type high_params: list(float)
:param low_params: Arrhenius Fitting Parameters used for low-P
:type low_params: list(float)
:param troe_params: Troe alpha, T1, T2, and T3 fitting parameters
:type troe_params: list(float)
:param colliders: names and collision enhancement factors for bath spc
:type colliders: list((str, float))
:return troe_str: ChemKin reaction string with Troe parameters
:rtype: str
"""
if ea_units == 'kcal/mol':
ea_factor = 1000
elif ea_units == 'cal/mol':
ea_factor = 1
else:
raise InputError(f"The units for Ea must be either 'kcal/mol' or 'cal/mol' but were input as {ea_units}")
assert len(high_params) == 3
assert len(low_params) == 3
assert len(troe_params) in (3, 4)
[high_a, high_n, high_ea] = high_params
# Write reaction header (with third body added) and high-pressure params
reaction = _format_rxn_str_for_pdep(reaction, pressure='all')
troe_str = '{0:<32s}{1:>10.3E}{2:>9.3f}{3:9.0f} /\n'.format(
reaction, high_a, high_n, ea_factor*high_ea)
# Write the collider efficiencies string
if colliders:
troe_str += _format_collider_string(colliders)
# Now write the low-pressure and Troe params
troe_str += _format_params_string('LOW', low_params, newline=True)
troe_str += _format_params_string('TROE', troe_params, newline=False)
return troe_str
def lindemann(reaction, high_params, low_params, colliders=(), ea_units='kcal/mol'):
""" Write the string containing the Lindemann fitting parameters
formatted for ChemKin input files
:param reaction: ChemKin formatted string with chemical equation
:type reaction: str
:param high_params: Arrhenius Fitting Parameters used for high-P
:type high_params: list(float)
:param low_params: Arrhenius Fitting Parameters used for low-P
:type low_params: list(float)
:param colliders: names and collision enhancement factors for bath spc
:type colliders: list((str, float))
:return lind_str: ChemKin reaction string with Lindemann parameters
:rtype: str
"""
if ea_units == 'kcal/mol':
ea_factor = 1000
elif ea_units == 'cal/mol':
ea_factor = 1
else:
raise InputError(f"The units for Ea must be either 'kcal/mol' or 'cal/mol' but were input as {ea_units}")
[high_a, high_n, high_ea] = high_params
# Write reaction header (with third body added) and high-pressure params
reaction = _format_rxn_str_for_pdep(reaction, pressure='low')
lind_str = '{0:<32s}{1:>10.3E}{2:>9.3f}{3:9.0f} /\n'.format(
reaction, high_a, high_n, ea_factor*high_ea)
# Write the collider efficiencies string
if colliders:
lind_str += _format_collider_string(colliders)
# Now write the low-pressure and Troe params
lind_str += _format_params_string('LOW', low_params, newline=False)
return lind_str
def plog(reaction, rate_params_dct, temp_dct=None, err_dct=None):
""" Write the string containing the PLOG fitting parameters
formatted for ChemKin input files.
:param reaction: ChemKin formatted string with chemical equation
:type reaction: str
:param rate_params_dct: Arrhenius fitting parameters at each pressure
:type rate_params_dct: dict[pressure: [rate params]]
:param temp_dct: temperature ranges for fits at each pressure
:type temp_dct: dict[pressure: [temps]]
:param err_dct: mean and max ftting errors at each pressure
:type err_dct: dict[pressure: [errs]]
:return plog_str: ChemKin reaction string with PLOG parameters
:rtype: str
"""
# Find nparams and ensure there are correct num in each dct entry
nparams = len(next(iter(rate_params_dct.values())))
assert nparams in (3, 6)
assert all(len(params) == nparams for params in rate_params_dct.values())
# Obtain a list of the pressures and sort from low to high pressure
pressures = [pressure for pressure in rate_params_dct.keys()
if pressure != 'high']
pressures.sort()
# Add fake high pressure parameters if they are not in the dictionary
if 'high' not in rate_params_dct:
if nparams == 3:
rate_params_dct['high'] = [1.00, 0.00, 0.00]
elif nparams == 6:
rate_params_dct['high'] = [1.00, 0.00, 0.00, 1.00, 0.00, 0.00]
# Build the reaction string with high-pressure params and any plog params
# Loop will build second ('DUPLICATE') section if double fit performed
p_str = ''
for i in range(nparams // 3):
if i == 1:
p_str += 'DUPLICATE\n'
# Build the initial string with the reaction and high-pressure params
high_a, high_n, high_ea = rate_params_dct['high'][3*i:3*i+3]
p_str += '{0:<32s}{1:>10.3E}{2:>9.3f}{3:9.0f} /\n'.format(
reaction, high_a, high_n, 1000*high_ea)
# Build the PLOG string for each pressure, other than the HighP Limit
for pressure in pressures:
pdep_a, pdep_n, pdep_ea = rate_params_dct[pressure][3*i:3*i+3]
p_str += '{0:>18s} /{1:>10.3f} '.format(
'PLOG', float(pressure))
p_str += '{0:>10.3E}{1:>9.3f}{2:9.0f} /\n'.format(
pdep_a, pdep_n, 1000*pdep_ea)
# Write string showing the temp fit range and fit errors
if temp_dct or err_dct:
p_str += _fit_info_str(pressures, temp_dct, err_dct)
return p_str
def chebyshev(reaction, high_params, alpha, tmin, tmax, pmin, pmax):
""" Write the string containing the Chebyshev fitting parameters
formatted for ChemKin input files.
:param reaction: ChemKin formatted string with chemical equation
:type reaction: str
:param high_params: Arrhenius Fitting Parameters used for high-P
:type high_params: list(float)
:param alpha: Chebyshev coefficient matrix
:type alpha: numpy.ndarray
:param tmin: minimum temperature Chebyshev model is defined
:type tmin: float
:param tmax: maximum temperature Chebyshev model is defined
:type tmax: float
:param pmin: minimum pressure Chebyshev model is defined
:type pmin: float
:return cheb_str: ChemKin reaction string with Chebyshev parameters
:rtype: str
"""
assert len(high_params) == 3
# assert alpha mat is a 2d matrix
[high_a, high_n, high_ea] = high_params
# Write reaction header (with third body added) and high-pressure params
reaction = _format_rxn_str_for_pdep(reaction, pressure='all')
cheb_str = '{0:<32s}{1:>10.3E}{2:>9.3f}{3:9.0f} /\n'.format(
reaction, high_a, high_n, 1000*high_ea)
# Write the temperature and pressure ranges
cheb_str += _format_params_string('TCHEB', (tmin, tmax), newline=True)
cheb_str += _format_params_string('PCHEB', (pmin, pmax), newline=True)
# Write the dimensions of the alpha matrix
nrows = len(alpha)
ncols = len(alpha[0])
cheb_str += '{0:>10s}/ {1:d} {2:d}\n'.format('CHEB', nrows, ncols)
# Write the parameters from the alpha matrix
for idx, row in enumerate(alpha):
newline = bool(idx+1 != nrows)
cheb_str += _format_params_string('CHEB', row, newline=newline)
return cheb_str
# Various formatting functions
def _fit_info_str(pressures, temp_dct, err_dct):
""" Write the string detailing the temperature ranges and fitting errors
associated with the rate-constant fits at each pressure.
:param pressures: pressures the k(T,P)s were calculated at
:type pressures: list(float)
:param temp_dct: temperature ranges (K) fits were done at each pressure
:type temp_dct: dict[pressure, [temp range]]
:param err_dct: errors associated with the fits at each pressure
:type err_dct: dict[pressure, [mean err, max err]]
:return inf_str: string containing all of the fitting info
:rtype: str
"""
# Make temp, err dcts empty if fxn receives None; add 'high' to pressures
temp_dct = temp_dct if temp_dct else {}
err_dct = err_dct if err_dct else {}
if 'high' in temp_dct or 'high' in err_dct:
pressures += ['high']
# Check the temp and err dcts have same presures as rate_dcts
if temp_dct:
assert set(pressures) == set(temp_dct.keys())
err_dct = err_dct if err_dct else {}
if err_dct:
assert set(pressures) == set(err_dct.keys())
# Write string showing the temp fit range and fit errors
inf_str = '! Info Regarding Rate Constant Fits\n'
for pressure in pressures:
if temp_dct:
[min_temp, max_temp] = temp_dct[pressure]
temps_str = '{0:.0f}-{1:.0f} K'.format(
min_temp, max_temp)
temp_range_str = 'Temps: {0:>12s}, '.format(
temps_str)
else:
temp_range_str = ''
if err_dct:
[mean_err, max_err] = err_dct[pressure]
err_str = '{0:11s} {1:>5.1f}%, {2:7s} {3:>5.1f}%'.format(
'MeanAbsErr:', mean_err, 'MaxErr:', max_err)
else:
err_str = ''
# Put together the who info string
if pressure != 'high':
pstr = '{0:<9.3f}'.format(pressure)
else:
pstr = '{0:<9s}'.format('High')
inf_str += '! Pressure: {0} {1} {2}\n'.format(
pstr, temp_range_str, err_str)
return inf_str
def _format_rxn_str_for_pdep(reaction, pressure='all'):
""" Add the bath gas M species to the reaction string for
pressure dependent reactions in the appropriate format.
:param reaction: chemical equation for the reaction
:type reaction: str
:param pressure: signifies the level of pressure dependence
:type pressure: str
:return: three_body_reaction: chemical equation with M body
:rtype: str
"""
# Determine format of M string to be added to reaction string
assert pressure in ('low', 'all')
if pressure == 'all':
m_str = ' (+M)'
else:
m_str = ' + M'
# Add the M string to both sides of the reaction string
[lhs, rhs] = reaction.split('=')
three_body_reaction = lhs + m_str + ' = ' + rhs + m_str
return three_body_reaction
def _format_collider_string(colliders):
""" Write the string for the bath gas collider and their efficiencies
for the Lindemann and Troe functional expressions:
:param colliders:
:type colliders: list(str)
:return: collider_str: ChemKin-format string with colliders
:rtype: str
"""
collider_str = ''.join(
('{0:s}/{1:4.3f}/ '.format(collider[0], collider[1])
for collider in colliders))
collider_str += '\n'
return collider_str
def _format_params_string(header, params, newline=False):
""" Write a string containing fitting params used for several
functional forms.
:param header: name of functional form the parameters correspond to
:type header: str
:param params: fitting parameters
:type params: list(float)
:param newline: signals whether to add a newline
:type newline: bool
:return: params_str: string containing the parameters
:rtype: str
"""
params_str = '{0:>10s}/ '.format(header.upper())
params_str += ''.join(('{0:12.3E}'.format(param) for param in params))
params_str += ' /'
if newline:
params_str += '\n'
return params_str
def _format_rxn_name(rxn_key):
""" Receives a rxn key from a param_dct and
writes it to a string that the above functions
can handle.
"""
rcts = rxn_key[0]
prds = rxn_key[1]
for idx, rct in enumerate(rcts):
if idx == 0:
rct_str = rct
else:
rct_str += '+' + rct
for idx, prd in enumerate(prds):
if idx == 0:
prd_str = prd
else:
prd_str += '+' + prd
rxn_name = rct_str + '=' + prd_str
return rxn_name | [
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6797,
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220,
220,
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220,
220,
460,
5412,
13,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
374,
310,
82,
796,
374,
87,
77,
62,
2539,
58,
15,
60,
198,
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778,
9310,
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2539,
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329,
4686,
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220,
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2073,
25,
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220,
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62,
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6,
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329,
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7,
1050,
9310,
2599,
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87,
6624,
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67,
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220,
220,
2073,
25,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
778,
67,
62,
2536,
15853,
705,
10,
6,
1343,
778,
67,
198,
220,
220,
220,
374,
87,
77,
62,
3672,
796,
374,
310,
62,
2536,
1343,
705,
11639,
1343,
778,
67,
62,
2536,
198,
220,
220,
220,
220,
198,
220,
220,
220,
1441,
374,
87,
77,
62,
3672
] | 2.389476 | 5,302 |
"""
Human
Search ideal tag
Is rep? Is sense?
tag simplification
"lizard in control of US gov" "democratics win midterms" "borders cause violence"
Find
Keywords
Yank
.txt
Reliability Assessment
Number of articles
Qualification
Compare? (John Baudrillard)
"""
import sys
import urllib
from urllib.request import urlopen
from googlesearch import search
from bs4 import BeautifulSoup
import newspaper
from newspaper import Article
from newspaper import fulltext
import numpy as np
import inspect
import requests
import nltk
nltk.download('punkt')
currentFuncName = lambda n=0: sys._getframe(n + 1).f_code.co_name
cnn_paper = newspaper.build('http://cnn.com')
if __name__ == "__main__":
runLoop()
| [
37811,
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37,
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37456,
299,
28,
15,
25,
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7,
77,
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352,
737,
69,
62,
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13,
1073,
62,
3672,
198,
66,
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62,
20189,
796,
7533,
13,
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10786,
4023,
1378,
66,
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13,
785,
11537,
628,
628,
628,
198,
361,
11593,
3672,
834,
6624,
366,
834,
12417,
834,
1298,
198,
220,
220,
220,
1057,
39516,
3419,
198
] | 2.976 | 250 |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import logging
import torch
from reagent.core.types import PreprocessedTrainingBatch
from reagent.training.world_model.seq2reward_trainer import Seq2RewardTrainer
logger = logging.getLogger(__name__)
| [
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7,
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8,
628
] | 3.322222 | 90 |
#Notebook Problem
import random
import time
print("Save your notes here, just type what you want to store.")
note1 = input("NOTE 1: ")
note2 = input("NOTE 2: ")
note3 = input("NOTE 3: ")
note4 = input("NOTE 4: ")
note5 = input("NOTE 5: ")
note6 = input("NOTE 6: ")
note7 = input("NOTE 7: ")
note8 = input("NOTE 8: ")
note9 = input("NOTE 9: ")
note10 = input("NOTE 10: ")
notes = [note1, note2, note3, note4, note5, note6, note7, note8, note9, note10]
print(notes)
| [
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18,
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19,
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11,
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21,
11,
3465,
22,
11,
3465,
23,
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60,
201,
198,
4798,
7,
17815,
8,
201,
198,
201,
198,
201,
198
] | 2.44 | 200 |
import discord
from discord.ext import commands
from urllib.parse import urlencode
import random
import aiohttp
import secrets
bot = commands.Bot(command_prefix='?')
@bot.event
# Retrieve query results from Wolfram Short Answer API
@bot.command(aliases=['eval'])
# Search Python3 Docs
@bot.command(aliases=['pyh'])
# Roll dice in NdN format
@bot.command()
# Pick random choice from 2
@bot.command(description='Heads or tails')
# Repeat a message N times
@bot.command()
# Lookup user join timestamp on server
@bot.command()
# Run the bot
bot.run(secrets.discord_token)
| [
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198
] | 2.769231 | 221 |
from requests import get
from bs4 import BeautifulSoup
import re
from datetime import datetime, timedelta
import pytz
# Ad class to centralize informations
#TODO Ajust this shit :/
# Get informations from pages | [
6738,
7007,
1330,
651,
198,
6738,
275,
82,
19,
1330,
23762,
50,
10486,
198,
11748,
302,
198,
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2,
51,
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317,
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428,
7510,
1058,
14,
198,
198,
2,
3497,
4175,
602,
422,
5468
] | 3.55 | 60 |
# Return the next leaf node in tree
# Given a tree, if query a leaf node, return the next leaf node. If the queried node is an internal node, return whatever you want. You can define the node structure. (Not a binary tree)
# Example:
# a
# z x
# w z y o b
# If I query 'z' return 'y'. If I query 'y', return 'o'. If I query 'b', return null.
print(nextLeaf(query))
# if not node:
# return None
# leafNodes = []
# tem = node
# while node:
# if not node.children:
# leafNodes.append(node.val)
# node = node.children
# if query in leafNodes:
# index = leafNodes[query]
# if leafNodes[index + 1]:
# return leafNodes[index + 1]
# return None
# node = tem.next
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2,
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2,
220,
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45,
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58,
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60,
198,
197,
197,
2,
220,
197,
361,
12835,
45,
4147,
58,
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1343,
352,
5974,
198,
197,
197,
2,
220,
197,
197,
7783,
12835,
45,
4147,
58,
9630,
1343,
352,
60,
198,
197,
197,
2,
220,
197,
7783,
6045,
198,
197,
197,
2,
10139,
796,
2169,
13,
19545,
220,
628
] | 2.328267 | 329 |
"""
This file has function containg main PPO algorithm
called in : main.py
"""
import time
from reply_buffer import *
from eval_score_and_trace_update import *
def train_model(value_model,
policy_model,
env,
policy_optimizer,
policy_optimization_epochs,
policy_sample_ratio,
policy_clip_range,
policy_model_max_grad_norm,
policy_stopping_kl,
entropy_loss_weight,
value_optimization_epochs,
value_optimizer,
value_sample_ratio,
value_clip_range,
value_model_max_grad_norm,
value_stopping_mse,
gamma,
lambda_,
r_1,
r_2,
r_3,
r_4,
sample,
n_sensor,
EPS,
evaluation_score):
"""
Args:
value_model: value model instance
policy_model: polica model instance
env: env class instance
policy_optimizer: policy optimizer -> adam
policy_optimization_epochs: no of epoch for policy training
policy_sample_ratio: no of trajectory to take for training
policy_clip_range: clipping parameter of policy loss
policy_model_max_grad_norm: maximum norm tolerance of policy optimization
policy_stopping_kl: tolerance for training of policy net
entropy_loss_weight: factor for entropy loss
value_optimization_epochs: no of epochs for value model
value_optimizer: value optimizer -> adam
value_sample_ratio: no of trajectory to take for training
value_clip_range: clipping parameter of value model loss
value_model_max_grad_norm: maximum norm tolerance of value optimization
value_stopping_mse: tolerance for trainig of value net
gamma: discount factor
lambda_: TD_lambda method factor
r_1: coefficient for reward function
r_2: coefficient for reward function
r_3 coefficient for reward function
r_4: coefficient for reward function
sample: number of ppo iteration
n_sensor: no of patches at the surface of cylinder
EPS: Tolerance for std
evaluation_score: evaluation score for post processing results
Returns: trajectory running time and time to run one iteration of ppo main algorithm
"""
# starting time to calculate time of trajectory run and each iteration of main algorithm
# getting variable for ppo algorithm from reply_buffer.py
traj_start_time = time.perf_counter()
states, actions, rewards, returns, logpas = fill_buffer(env, sample, n_sensor, gamma, r_1, r_2, r_3, r_4)
traj_time = (time.perf_counter() - traj_start_time)
# get V_pi for the state values
values_pi = value_model(torch.from_numpy(states)).squeeze().detach().numpy()
# compute GAEs of taken actions and the obtained rewards
gaes = calculate_gaes(values_pi, rewards, gamma, lambda_)
gaes = (gaes - gaes.mean()) / (gaes.std() + EPS)
# no of trajectories
n_samples = len(actions)
for q in range(policy_optimization_epochs):
# ramdom selection of trajectories from the reply buffer
batch_size = int(policy_sample_ratio * n_samples)
batch_idxs = np.random.choice(n_samples, batch_size, replace=False)
# get the data for chosen random selected trajectory
states_batch = states[batch_idxs]
actions_batch = actions[batch_idxs]
gaes_batch = gaes[batch_idxs]
logpas_batch = logpas[batch_idxs]
# log probabilities and entropy for randomly chosen trajectory
logpas_pred, entropies_pred = policy_model.get_predictions(states_batch[:, :-1, :], actions_batch)
# ratio of log probability to calculate the loss and clipping of policy loss
# compute entropy loss
ratios = (logpas_pred - torch.from_numpy(logpas_batch)).exp()
pi_obj = torch.from_numpy(gaes_batch) * ratios
pi_obj_clipped = torch.from_numpy(gaes_batch) * ratios.clamp(1.0 - policy_clip_range, 1.0 + policy_clip_range)
policy_loss = -torch.min(pi_obj, pi_obj_clipped).mean()
entropy_loss = -entropies_pred.mean() * entropy_loss_weight
# total loss (entropy loss + policy loss) back propagation
policy_optimizer.zero_grad()
(policy_loss + entropy_loss).backward()
torch.nn.utils.clip_grad_norm_(policy_model.parameters(), policy_model_max_grad_norm)
policy_optimizer.step()
# checking for optimization in range of tolerance
with torch.no_grad():
logpas_pred_all, _ = policy_model.get_predictions(states[:, :-1, :], actions)
kl = (torch.from_numpy(logpas) - logpas_pred_all).mean()
if kl.item() > policy_stopping_kl:
print(f'kl smaller than tolrence, {q} and {kl.item()}')
break
model_trace_update(policy_model, sample)
for q in range(value_optimization_epochs):
# ramdom selection of trajectories from the reply buffer
batch_size = int(value_sample_ratio * n_samples)
batch_idxs = np.random.choice(n_samples, batch_size, replace=False)
# get the data for chosen random selected trajectory
states_batch = states[batch_idxs]
returns_batch = returns[batch_idxs]
values_batch = values_pi[batch_idxs]
# getting V_pi for randomly selected trajectories in reply buffer
values_pred = value_model(torch.from_numpy(states_batch)).squeeze()
values_pred_clipped = torch.from_numpy(values_batch) + (values_pred - torch.from_numpy(values_batch)).clamp(
-value_clip_range, value_clip_range)
# critic loss
v_loss = (torch.from_numpy(returns_batch) - values_pred).pow(2)
v_loss_clipped = (torch.from_numpy(returns_batch) - values_pred_clipped).pow(2)
value_loss = torch.max(v_loss, v_loss_clipped).mul(0.5).mean()
# critic loss optimization
value_optimizer.zero_grad()
value_loss.backward()
torch.nn.utils.clip_grad_norm_(value_model.parameters(), value_model_max_grad_norm)
value_optimizer.step()
# checking for optimization in range of tolerance
with torch.no_grad():
values_pred_all = value_model(torch.from_numpy(states)).squeeze()
mse = (torch.from_numpy(values_pi) - values_pred_all).pow(2).mul(0.5).mean()
if mse.item() > value_stopping_mse:
print(f'mse smaller than tolrence, {q}, {mse.item}')
break
# saving value model
saving_value_model(value_model, sample)
# computation time to complete one iteration of main PPO algorithm
epoch_time = (time.perf_counter() - traj_start_time)
# evaluation score at the end of iteration
score = evaluate_score(rewards, sample)
evaluation_score.append(score)
return traj_time, epoch_time
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] | 2.379751 | 2,973 |
#!/usr/bin/env python3
# coding=utf-8
#
# Copyright (c) 2021 Huawei Device Co., Ltd.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from xdevice import DeviceError
class HdcError(DeviceError):
"""
Raised when there is an error in hdc operations.
"""
class HdcCommandRejectedException(HdcError):
"""
Exception thrown when hdc refuses a command.
"""
class ShellCommandUnresponsiveException(HdcError):
"""
Exception thrown when a shell command executed on a device takes too long
to send its output.
"""
class DeviceUnresponsiveException(HdcError):
"""
Exception thrown when a shell command executed on a device takes too long
to send its output.
"""
| [
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] | 3.108374 | 406 |
# (C) Datadog, Inc. 2010-2018
# All rights reserved
# Licensed under Simplified BSD License (see LICENSE)
import subprocess
import os
import time
import pytest
import mock
from datadog_checks.postgres import PostgreSql
from .common import HOST, PORT, USER, PASSWORD, DB_NAME
HERE = os.path.dirname(os.path.abspath(__file__))
@pytest.fixture
@pytest.fixture(scope="session")
def postgres_standalone():
"""
Start a standalone postgres server requiring authentication before running a
test and stopping it afterwards.
If there's any problem executing docker-compose, let the exception bubble
up.
"""
env = os.environ
args = [
"docker-compose",
"-f", os.path.join(HERE, 'compose', 'standalone.compose')
]
subprocess.check_call(args + ["up", "-d"], env=env)
# waiting for PG to start
attempts = 0
while True:
if attempts > 10:
subprocess.check_call(args + ["down"], env=env)
raise Exception("PostgreSQL boot timed out!")
output = subprocess.check_output([
"docker",
"inspect",
"--format='{{json .State.Health.Status}}'",
"compose_postgres_1"])
# we get a json string output from docker
if output.strip() == "'\"healthy\"'":
break
attempts += 1
time.sleep(1)
yield
subprocess.check_call(args + ["down"], env=env)
@pytest.fixture
@pytest.fixture
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] | 2.477157 | 591 |
from google import search
import pprint
KEYWORDS = 'duyetdev'
data = []
for d in search(KEYWORDS, tld='com.vn', lang='vi', stop=10):
data.append(d)
print pprint.pprint(data) | [
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] | 2.565217 | 69 |
# import the necessary packages
from imutils.video import VideoStream
from imutils.video import FPS
import sys
import os
import numpy as np
import argparse
import imutils
import time
import cv2
sys.path.append(os.path.abspath('./modules/'))
import detection
import helpers
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video", type=str,
default = 0,
help="path to input video file")
ap.add_argument("-fv", "--flip_video", type=bool, default=False, help="Flip video orientation (if the camera is upside down)")
ap.add_argument("-t", "--tracker", type=str, default="kcf",
help="OpenCV object tracker type")
ap.add_argument("-o", "--output", type=str, default=None,
help="Should the video capture write to an output file?")
ap.add_argument('-c', '--config', required=False,
default='yolo.cfg',
help = 'path to yolo config file, defaults to yolo.cgf')
ap.add_argument('-w', '--weights', required=False,
default='yolov3.weights',
help = 'path to yolo pre-trained weights, defaults to yolov3.weights.')
ap.add_argument('-cl', '--classes', required=False,
default = 'yolo-classes.txt',
help = 'path to text file containing class names, defaults to yolo-classes.txt')
ap.add_argument('-cf', '--confidence', required=False,
default = 0.5,
help = 'confidence threshold for object detection, defaults to 0.5')
args = ap.parse_args()
# extract the OpenCV version info
(major, minor) = cv2.__version__.split(".")[:2]
# if we are using OpenCV 3.2 OR BEFORE, we can use a special factory
# function to create our object tracker
if int(major) == 3 and int(minor) < 3:
tracker = cv2.Tracker_create(args.tracker.upper())
# otherwise, for OpenCV 3.3 OR NEWER, we need to explicity call the
# approrpiate object tracker constructor:
else:
# initialize a dictionary that maps strings to their corresponding
# OpenCV object tracker implementations
OPENCV_OBJECT_TRACKERS = {
"csrt": cv2.TrackerCSRT_create, # High tracking accuracy at the expense of FPS
"kcf": cv2.TrackerKCF_create, # Lower tracking accuracy for higher FPS
"mil": cv2.TrackerMIL_create
}
if(hasattr(cv2,'cv2.TrackerMOSSE_create')):
OPENCV_OBJECT_TRACKERS["mosse"] = cv2.TrackerMOSSE_create
if(hasattr(cv2,'cv2.TrackerMIL_create')):
OPENCV_OBJECT_TRACKERS["mil"] = cv2.TrackerMIL_create
if(hasattr(cv2,'cv2.TrackerBoosting_create')):
OPENCV_OBJECT_TRACKERS["boosting"] = cv2.TrackerBoosting_create
if(hasattr(cv2,'cv2.TrackerTLD_create')):
OPENCV_OBJECT_TRACKERS["tld"] = cv2.TrackerTLD_create
if(hasattr(cv2,'cv2.TrackerMedianFlow_create')):
OPENCV_OBJECT_TRACKERS["medianflow"] = cv2.TrackerMedianFlow_create
# grab the appropriate object tracker using our dictionary of
# OpenCV object tracker objects
tracker = OPENCV_OBJECT_TRACKERS[args.tracker]()
# initialize the bounding box coordinates of the object we are going
# to track
initBB = None
rotateCode = None
# if a video path was not supplied, grab the reference to the web cam
if not args.video:
print("[INFO] starting video stream...")
vs = VideoStream(src=0,resolution=(480,640)).start()
time.sleep(1.0)
# otherwise, grab a reference to the video file
else:
vs = cv2.VideoCapture(args.video)
# initialize the FPS throughput estimator
fps = None
# If output writing we will need to initialise thiw
writer = None
zeros = None
# loop over frames from the video stream
while True:
# grab the current frame, then handle if we are using a
# VideoStream or VideoCapture object
frame = vs.read()
frame = frame[1] if args.video else frame
# check to see if we have reached the end of the stream
if frame is None:
break
# resize the frame (so we can process it faster) and grab the
# frame dimensions
frame = imutils.resize(frame, width=800)
(H, W) = frame.shape[:2]
# check if the frame needs to be rotated
if args.flip_video:
frame = cv2.flip(frame,-1)
if(args.output and writer is None):
# Define the codec and create VideoWriter object
fourcc = cv2.VideoWriter_fourcc(*"DIVX")
writer = cv2.VideoWriter(args.output + '.mp4',fourcc, 20.0, (W,H), True)
zeros = np.zeros((H, W), dtype="uint8")
# check to see if we are currently tracking an object
if initBB is not None:
# grab the new bounding box coordinates of the object
(success, box) = tracker.update(frame)
# check to see if the tracking was a success
if success:
(x, y, w, h) = [int(v) for v in box]
cv2.rectangle(frame, (x, y), (x + w, y + h),
(0, 255, 0), 2)
# update the FPS counter
fps.update()
fps.stop()
# initialize the set of information we'll be displaying on
# the frame
info = [
("Tracker", args.tracker),
("Success", "Yes" if success else "No"),
("FPS", "{:.2f}".format(fps.fps())),
]
# loop over the info tuples and draw them on our frame
for (i, (k, v)) in enumerate(info):
text = "{}: {}".format(k, v)
cv2.putText(frame, text, (10, H - ((i * 20) + 20)),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
else:
initBB = detection.GolfBallDetection(frame)
if(initBB):
# start OpenCV object tracker using the supplied bounding box
# coordinates, then start the FPS throughput estimator as well
tracker.init(frame, initBB)
fps = FPS().start()
# show the output frame
cv2.imshow("Frame", frame)
if(writer):
# write the output frame to file
writer.write(frame)
key = cv2.waitKey(1) & 0xFF
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
# if we are using a webcam, release the pointer
if not args.video:
vs.stop()
# otherwise, release the file pointer
else:
vs.release()
if(writer):
writer.release()
# close all windows
cv2.destroyAllWindows() | [
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198,
17772,
25,
201,
198,
220,
220,
220,
1303,
41216,
257,
22155,
326,
8739,
13042,
284,
511,
11188,
201,
198,
220,
220,
220,
1303,
4946,
33538,
2134,
30013,
25504,
201,
198,
220,
220,
220,
13349,
24181,
53,
62,
9864,
23680,
62,
5446,
8120,
4877,
796,
1391,
201,
198,
220,
220,
220,
220,
220,
220,
220,
366,
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17034,
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17,
13,
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62,
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1303,
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262,
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1303,
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220,
220,
220,
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13,
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13,
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62,
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220,
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220,
220,
220,
220,
13349,
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53,
62,
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62,
5446,
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4877,
14692,
39521,
278,
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796,
269,
85,
17,
13,
35694,
45686,
278,
62,
17953,
201,
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220,
220,
220,
611,
7,
10134,
35226,
7,
33967,
17,
4032,
33967,
17,
13,
35694,
51,
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62,
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11537,
2599,
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220,
220,
220,
220,
220,
220,
220,
13349,
24181,
53,
62,
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62,
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335,
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51,
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62,
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220,
220,
611,
7,
10134,
35226,
7,
33967,
17,
4032,
33967,
17,
13,
35694,
9921,
666,
37535,
62,
17953,
11537,
2599,
201,
198,
220,
220,
220,
220,
220,
220,
220,
13349,
24181,
53,
62,
9864,
23680,
62,
5446,
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1150,
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796,
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17,
13,
35694,
9921,
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37535,
62,
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220,
220,
220,
1303,
5552,
262,
5035,
2134,
30013,
1262,
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22155,
286,
201,
198,
220,
220,
220,
1303,
4946,
33538,
2134,
30013,
5563,
201,
198,
220,
220,
220,
30013,
796,
13349,
24181,
53,
62,
9864,
23680,
62,
5446,
8120,
4877,
58,
22046,
13,
2213,
10735,
60,
3419,
201,
198,
201,
198,
2,
41216,
262,
5421,
278,
3091,
22715,
286,
262,
2134,
356,
389,
1016,
201,
198,
2,
284,
2610,
201,
198,
15003,
15199,
796,
6045,
201,
198,
10599,
378,
10669,
796,
6045,
201,
198,
201,
198,
2,
611,
257,
2008,
3108,
373,
407,
14275,
11,
5552,
262,
4941,
284,
262,
3992,
12172,
201,
198,
361,
407,
26498,
13,
15588,
25,
201,
198,
220,
220,
220,
3601,
7203,
58,
10778,
60,
3599,
2008,
4269,
9313,
8,
201,
198,
220,
220,
220,
3691,
796,
7623,
12124,
7,
10677,
28,
15,
11,
29268,
16193,
22148,
11,
31102,
29720,
9688,
3419,
201,
198,
220,
220,
220,
640,
13,
42832,
7,
16,
13,
15,
8,
201,
198,
2,
4306,
11,
5552,
257,
4941,
284,
262,
2008,
2393,
201,
198,
17772,
25,
201,
198,
220,
220,
220,
3691,
796,
269,
85,
17,
13,
10798,
49630,
7,
22046,
13,
15588,
8,
201,
198,
2,
41216,
262,
22082,
41997,
3959,
1352,
201,
198,
29647,
796,
6045,
201,
198,
201,
198,
2,
1002,
5072,
3597,
356,
481,
761,
284,
4238,
786,
294,
14246,
201,
198,
16002,
796,
6045,
201,
198,
9107,
418,
796,
6045,
201,
198,
201,
198,
2,
9052,
625,
13431,
422,
262,
2008,
4269,
201,
198,
4514,
6407,
25,
201,
198,
220,
220,
220,
1303,
5552,
262,
1459,
5739,
11,
788,
5412,
611,
356,
389,
1262,
257,
201,
198,
220,
220,
220,
1303,
7623,
12124,
393,
7623,
49630,
2134,
201,
198,
220,
220,
220,
5739,
796,
3691,
13,
961,
3419,
201,
198,
220,
220,
220,
5739,
796,
5739,
58,
16,
60,
611,
26498,
13,
15588,
2073,
5739,
201,
198,
220,
220,
220,
1303,
2198,
284,
766,
611,
356,
423,
4251,
262,
886,
286,
262,
4269,
201,
198,
220,
220,
220,
611,
5739,
318,
6045,
25,
201,
198,
220,
220,
220,
220,
220,
220,
220,
2270,
201,
198,
220,
220,
220,
1303,
47558,
262,
5739,
357,
568,
356,
460,
1429,
340,
5443,
8,
290,
5552,
262,
201,
198,
220,
220,
220,
1303,
5739,
15225,
201,
198,
220,
220,
220,
5739,
796,
545,
26791,
13,
411,
1096,
7,
14535,
11,
9647,
28,
7410,
8,
201,
198,
220,
220,
220,
357,
39,
11,
370,
8,
796,
5739,
13,
43358,
58,
25,
17,
60,
201,
198,
220,
220,
220,
1303,
2198,
611,
262,
5739,
2476,
284,
307,
38375,
201,
198,
220,
220,
220,
611,
26498,
13,
2704,
541,
62,
15588,
25,
201,
198,
220,
220,
220,
220,
220,
220,
220,
5739,
796,
269,
85,
17,
13,
2704,
541,
7,
14535,
12095,
16,
8,
201,
198,
201,
198,
220,
220,
220,
611,
7,
22046,
13,
22915,
290,
6260,
318,
6045,
2599,
201,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
2896,
500,
262,
40481,
290,
2251,
7623,
34379,
2134,
201,
198,
220,
220,
220,
220,
220,
220,
220,
1440,
535,
796,
269,
85,
17,
13,
10798,
34379,
62,
14337,
535,
46491,
1,
33569,
55,
4943,
201,
198,
220,
220,
220,
220,
220,
220,
220,
6260,
796,
269,
85,
17,
13,
10798,
34379,
7,
22046,
13,
22915,
1343,
45302,
3149,
19,
3256,
14337,
535,
11,
1160,
13,
15,
11,
357,
54,
11,
39,
828,
6407,
8,
201,
198,
220,
220,
220,
220,
220,
220,
220,
1976,
27498,
796,
45941,
13,
9107,
418,
19510,
39,
11,
370,
828,
288,
4906,
2625,
28611,
23,
4943,
201,
198,
201,
198,
220,
220,
220,
1303,
2198,
284,
766,
611,
356,
389,
3058,
9646,
281,
2134,
201,
198,
220,
220,
220,
611,
2315,
15199,
318,
407,
6045,
25,
201,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
5552,
262,
649,
5421,
278,
3091,
22715,
286,
262,
2134,
201,
198,
220,
220,
220,
220,
220,
220,
220,
357,
13138,
11,
3091,
8,
796,
30013,
13,
19119,
7,
14535,
8,
201,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
2198,
284,
766,
611,
262,
9646,
373,
257,
1943,
201,
198,
220,
220,
220,
220,
220,
220,
220,
611,
1943,
25,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
87,
11,
331,
11,
266,
11,
289,
8,
796,
685,
600,
7,
85,
8,
329,
410,
287,
3091,
60,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
269,
85,
17,
13,
2554,
9248,
7,
14535,
11,
357,
87,
11,
331,
828,
357,
87,
1343,
266,
11,
331,
1343,
289,
828,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
15,
11,
14280,
11,
657,
828,
362,
8,
201,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
4296,
262,
22082,
3753,
201,
198,
220,
220,
220,
220,
220,
220,
220,
32977,
13,
19119,
3419,
201,
198,
220,
220,
220,
220,
220,
220,
220,
32977,
13,
11338,
3419,
201,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
41216,
262,
900,
286,
1321,
356,
1183,
307,
19407,
319,
201,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
262,
5739,
201,
198,
220,
220,
220,
220,
220,
220,
220,
7508,
796,
685,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5855,
35694,
1600,
26498,
13,
2213,
10735,
828,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5855,
33244,
1600,
366,
5297,
1,
611,
1943,
2073,
366,
2949,
12340,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5855,
37,
3705,
1600,
45144,
25,
13,
17,
69,
92,
1911,
18982,
7,
29647,
13,
29647,
28955,
828,
201,
198,
220,
220,
220,
220,
220,
220,
220,
2361,
201,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
9052,
625,
262,
7508,
12777,
2374,
290,
3197,
606,
319,
674,
5739,
201,
198,
220,
220,
220,
220,
220,
220,
220,
329,
357,
72,
11,
357,
74,
11,
410,
4008,
287,
27056,
378,
7,
10951,
2599,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2420,
796,
45144,
38362,
23884,
1911,
18982,
7,
74,
11,
410,
8,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
269,
85,
17,
13,
1996,
8206,
7,
14535,
11,
2420,
11,
357,
940,
11,
367,
532,
14808,
72,
1635,
1160,
8,
1343,
1160,
36911,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
269,
85,
17,
13,
37,
35830,
62,
39,
4877,
13909,
56,
62,
48913,
16437,
55,
11,
657,
13,
21,
11,
357,
15,
11,
657,
11,
14280,
828,
362,
8,
201,
198,
220,
220,
220,
2073,
25,
201,
198,
220,
220,
220,
220,
220,
220,
220,
2315,
15199,
796,
13326,
13,
38,
4024,
23410,
11242,
3213,
7,
14535,
8,
201,
198,
220,
220,
220,
220,
220,
220,
220,
611,
7,
15003,
15199,
2599,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
923,
4946,
33538,
2134,
30013,
1262,
262,
14275,
5421,
278,
3091,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
22715,
11,
788,
923,
262,
22082,
41997,
3959,
1352,
355,
880,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
30013,
13,
15003,
7,
14535,
11,
2315,
15199,
8,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
32977,
796,
22082,
22446,
9688,
3419,
201,
198,
201,
198,
220,
220,
220,
1303,
905,
262,
5072,
5739,
201,
198,
220,
220,
220,
269,
85,
17,
13,
320,
12860,
7203,
19778,
1600,
5739,
8,
201,
198,
220,
220,
220,
611,
7,
16002,
2599,
201,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
3551,
262,
5072,
5739,
284,
2393,
201,
198,
220,
220,
220,
220,
220,
220,
220,
6260,
13,
13564,
7,
14535,
8,
201,
198,
201,
198,
220,
220,
220,
1994,
796,
269,
85,
17,
13,
17077,
9218,
7,
16,
8,
1222,
657,
87,
5777,
201,
198,
220,
220,
220,
1303,
611,
262,
4600,
80,
63,
1994,
373,
12070,
11,
2270,
422,
262,
9052,
201,
198,
220,
220,
220,
611,
1994,
6624,
2760,
7203,
80,
1,
2599,
201,
198,
220,
220,
220,
220,
220,
220,
220,
2270,
201,
198,
2,
611,
356,
389,
1262,
257,
49823,
11,
2650,
262,
17562,
201,
198,
361,
407,
26498,
13,
15588,
25,
201,
198,
220,
220,
220,
3691,
13,
11338,
3419,
201,
198,
2,
4306,
11,
2650,
262,
2393,
17562,
201,
198,
17772,
25,
201,
198,
220,
220,
220,
3691,
13,
20979,
3419,
201,
198,
201,
198,
361,
7,
16002,
2599,
201,
198,
220,
220,
220,
6260,
13,
20979,
3419,
201,
198,
201,
198,
2,
1969,
477,
9168,
201,
198,
33967,
17,
13,
41659,
3237,
11209,
3419
] | 2.434816 | 2,631 |
import binascii
import sys
PY26 = sys.version_info[0] == 2 and sys.version_info[1] <= 6
if PY26:
import struct
class PgpdumpException(Exception):
'''Base exception class raised by any parsing errors, etc.'''
pass
# 256 values corresponding to each possible byte
CRC24_TABLE = (
0x000000, 0x864cfb, 0x8ad50d, 0x0c99f6, 0x93e6e1, 0x15aa1a, 0x1933ec,
0x9f7f17, 0xa18139, 0x27cdc2, 0x2b5434, 0xad18cf, 0x3267d8, 0xb42b23,
0xb8b2d5, 0x3efe2e, 0xc54e89, 0x430272, 0x4f9b84, 0xc9d77f, 0x56a868,
0xd0e493, 0xdc7d65, 0x5a319e, 0x64cfb0, 0xe2834b, 0xee1abd, 0x685646,
0xf72951, 0x7165aa, 0x7dfc5c, 0xfbb0a7, 0x0cd1e9, 0x8a9d12, 0x8604e4,
0x00481f, 0x9f3708, 0x197bf3, 0x15e205, 0x93aefe, 0xad50d0, 0x2b1c2b,
0x2785dd, 0xa1c926, 0x3eb631, 0xb8faca, 0xb4633c, 0x322fc7, 0xc99f60,
0x4fd39b, 0x434a6d, 0xc50696, 0x5a7981, 0xdc357a, 0xd0ac8c, 0x56e077,
0x681e59, 0xee52a2, 0xe2cb54, 0x6487af, 0xfbf8b8, 0x7db443, 0x712db5,
0xf7614e, 0x19a3d2, 0x9fef29, 0x9376df, 0x153a24, 0x8a4533, 0x0c09c8,
0x00903e, 0x86dcc5, 0xb822eb, 0x3e6e10, 0x32f7e6, 0xb4bb1d, 0x2bc40a,
0xad88f1, 0xa11107, 0x275dfc, 0xdced5b, 0x5aa1a0, 0x563856, 0xd074ad,
0x4f0bba, 0xc94741, 0xc5deb7, 0x43924c, 0x7d6c62, 0xfb2099, 0xf7b96f,
0x71f594, 0xee8a83, 0x68c678, 0x645f8e, 0xe21375, 0x15723b, 0x933ec0,
0x9fa736, 0x19ebcd, 0x8694da, 0x00d821, 0x0c41d7, 0x8a0d2c, 0xb4f302,
0x32bff9, 0x3e260f, 0xb86af4, 0x2715e3, 0xa15918, 0xadc0ee, 0x2b8c15,
0xd03cb2, 0x567049, 0x5ae9bf, 0xdca544, 0x43da53, 0xc596a8, 0xc90f5e,
0x4f43a5, 0x71bd8b, 0xf7f170, 0xfb6886, 0x7d247d, 0xe25b6a, 0x641791,
0x688e67, 0xeec29c, 0x3347a4, 0xb50b5f, 0xb992a9, 0x3fde52, 0xa0a145,
0x26edbe, 0x2a7448, 0xac38b3, 0x92c69d, 0x148a66, 0x181390, 0x9e5f6b,
0x01207c, 0x876c87, 0x8bf571, 0x0db98a, 0xf6092d, 0x7045d6, 0x7cdc20,
0xfa90db, 0x65efcc, 0xe3a337, 0xef3ac1, 0x69763a, 0x578814, 0xd1c4ef,
0xdd5d19, 0x5b11e2, 0xc46ef5, 0x42220e, 0x4ebbf8, 0xc8f703, 0x3f964d,
0xb9dab6, 0xb54340, 0x330fbb, 0xac70ac, 0x2a3c57, 0x26a5a1, 0xa0e95a,
0x9e1774, 0x185b8f, 0x14c279, 0x928e82, 0x0df195, 0x8bbd6e, 0x872498,
0x016863, 0xfad8c4, 0x7c943f, 0x700dc9, 0xf64132, 0x693e25, 0xef72de,
0xe3eb28, 0x65a7d3, 0x5b59fd, 0xdd1506, 0xd18cf0, 0x57c00b, 0xc8bf1c,
0x4ef3e7, 0x426a11, 0xc426ea, 0x2ae476, 0xaca88d, 0xa0317b, 0x267d80,
0xb90297, 0x3f4e6c, 0x33d79a, 0xb59b61, 0x8b654f, 0x0d29b4, 0x01b042,
0x87fcb9, 0x1883ae, 0x9ecf55, 0x9256a3, 0x141a58, 0xefaaff, 0x69e604,
0x657ff2, 0xe33309, 0x7c4c1e, 0xfa00e5, 0xf69913, 0x70d5e8, 0x4e2bc6,
0xc8673d, 0xc4fecb, 0x42b230, 0xddcd27, 0x5b81dc, 0x57182a, 0xd154d1,
0x26359f, 0xa07964, 0xace092, 0x2aac69, 0xb5d37e, 0x339f85, 0x3f0673,
0xb94a88, 0x87b4a6, 0x01f85d, 0x0d61ab, 0x8b2d50, 0x145247, 0x921ebc,
0x9e874a, 0x18cbb1, 0xe37b16, 0x6537ed, 0x69ae1b, 0xefe2e0, 0x709df7,
0xf6d10c, 0xfa48fa, 0x7c0401, 0x42fa2f, 0xc4b6d4, 0xc82f22, 0x4e63d9,
0xd11cce, 0x575035, 0x5bc9c3, 0xdd8538
)
def crc24(data):
'''Implementation of the CRC-24 algorithm used by OpenPGP.'''
# CRC-24-Radix-64
# x24 + x23 + x18 + x17 + x14 + x11 + x10 + x7 + x6
# + x5 + x4 + x3 + x + 1 (OpenPGP)
# 0x864CFB / 0xDF3261 / 0xC3267D
crc = 0x00b704ce
# this saves a bunch of slower global accesses
crc_table = CRC24_TABLE
for byte in data:
tbl_idx = ((crc >> 16) ^ byte) & 0xff
crc = (crc_table[tbl_idx] ^ (crc << 8)) & 0x00ffffff
return crc
def get_int2(data, offset):
'''Pull two bytes from data at offset and return as an integer.'''
return (data[offset] << 8) + data[offset + 1]
def get_int4(data, offset):
'''Pull four bytes from data at offset and return as an integer.'''
return ((data[offset] << 24) + (data[offset + 1] << 16) +
(data[offset + 2] << 8) + data[offset + 3])
def get_int8(data, offset):
'''Pull eight bytes from data at offset and return as an integer.'''
return (get_int4(data, offset) << 32) + get_int4(data, offset + 4)
def get_mpi(data, offset):
'''Gets a multi-precision integer as per RFC-4880.
Returns the MPI and the new offset.
See: http://tools.ietf.org/html/rfc4880#section-3.2'''
mpi_len = get_int2(data, offset)
offset += 2
to_process = (mpi_len + 7) // 8
mpi = 0
i = -4
for i in range(0, to_process - 3, 4):
mpi <<= 32
mpi += get_int4(data, offset + i)
for j in range(i + 4, to_process):
mpi <<= 8
mpi += data[offset + j]
# Python 3.2 and later alternative:
#mpi = int.from_bytes(data[offset:offset + to_process], byteorder='big')
offset += to_process
return mpi, offset
def get_hex_data(data, offset, byte_count):
'''Pull the given number of bytes from data at offset and return as a
hex-encoded string.'''
key_data = data[offset:offset + byte_count]
if PY26:
key_data = buffer(key_data)
key_id = binascii.hexlify(key_data)
return key_id.upper()
def get_key_id(data, offset):
'''Pull eight bytes from data at offset and return as a 16-byte hex-encoded
string.'''
return get_hex_data(data, offset, 8)
def get_int_bytes(data):
'''Get the big-endian byte form of an integer or MPI.'''
hexval = '%X' % data
new_len = (len(hexval) + 1) // 2 * 2
hexval = hexval.zfill(new_len)
return binascii.unhexlify(hexval.encode('ascii'))
def pack_data(data):
'''Pack iterable of binary data into a bytestring if necessary.'''
if PY26:
return struct.pack('%dB' % len(data), *data)
return data
def same_key(key_a, key_b):
'''Comparison function for key ID or fingerprint strings, taking into
account varying length.'''
if len(key_a) == len(key_b):
return key_a == key_b
elif len(key_a) < len(key_b):
return key_b.endswith(key_a)
else:
return key_a.endswith(key_b)
| [
11748,
9874,
292,
979,
72,
198,
11748,
25064,
198,
198,
47,
56,
2075,
796,
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13,
9641,
62,
10951,
58,
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69,
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15,
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23,
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17,
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24,
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66,
11,
198,
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24,
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2996,
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657,
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67,
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66,
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22,
66,
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3682,
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17,
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11,
657,
25306,
19,
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21,
67,
19,
11,
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11,
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20,
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24,
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8,
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286,
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12,
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47,
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21,
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19,
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18,
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2124,
1343,
352,
357,
11505,
6968,
47,
8,
198,
220,
220,
220,
1303,
657,
87,
39570,
22495,
33,
1220,
657,
87,
8068,
18,
30057,
1220,
657,
87,
34,
18,
25674,
35,
198,
220,
220,
220,
1067,
66,
796,
657,
87,
405,
65,
32869,
344,
198,
220,
220,
220,
1303,
428,
16031,
257,
7684,
286,
13611,
3298,
1895,
274,
198,
220,
220,
220,
1067,
66,
62,
11487,
796,
45623,
1731,
62,
38148,
198,
220,
220,
220,
329,
18022,
287,
1366,
25,
198,
220,
220,
220,
220,
220,
220,
220,
256,
2436,
62,
312,
87,
796,
14808,
66,
6015,
9609,
1467,
8,
10563,
18022,
8,
1222,
657,
47596,
198,
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220,
220,
220,
220,
220,
220,
1067,
66,
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66,
6015,
62,
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83,
2436,
62,
312,
87,
60,
10563,
357,
66,
6015,
9959,
807,
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405,
12927,
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220,
1441,
1067,
66,
628,
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4299,
651,
62,
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17,
7,
7890,
11,
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2599,
198,
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220,
220,
705,
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42940,
734,
9881,
422,
1366,
379,
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290,
1441,
355,
281,
18253,
2637,
7061,
198,
220,
220,
220,
1441,
357,
7890,
58,
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60,
9959,
807,
8,
1343,
1366,
58,
28968,
1343,
352,
60,
628,
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4299,
651,
62,
600,
19,
7,
7890,
11,
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2599,
198,
220,
220,
220,
705,
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42940,
1440,
9881,
422,
1366,
379,
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290,
1441,
355,
281,
18253,
2637,
7061,
198,
220,
220,
220,
1441,
14808,
7890,
58,
28968,
60,
9959,
1987,
8,
1343,
357,
7890,
58,
28968,
1343,
352,
60,
9959,
1467,
8,
1343,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
7890,
58,
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1343,
362,
60,
9959,
807,
8,
1343,
1366,
58,
28968,
1343,
513,
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628,
198,
4299,
651,
62,
600,
23,
7,
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11,
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2599,
198,
220,
220,
220,
705,
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42940,
3624,
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379,
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290,
1441,
355,
281,
18253,
2637,
7061,
198,
220,
220,
220,
1441,
357,
1136,
62,
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19,
7,
7890,
11,
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8,
9959,
3933,
8,
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651,
62,
600,
19,
7,
7890,
11,
11677,
1343,
604,
8,
628,
198,
4299,
651,
62,
3149,
72,
7,
7890,
11,
11677,
2599,
198,
220,
220,
220,
705,
7061,
38,
1039,
257,
5021,
12,
3866,
16005,
18253,
355,
583,
30978,
12,
2780,
1795,
13,
198,
220,
220,
220,
16409,
262,
4904,
40,
290,
262,
649,
11677,
13,
198,
220,
220,
220,
4091,
25,
2638,
1378,
31391,
13,
1155,
69,
13,
2398,
14,
6494,
14,
81,
16072,
2780,
1795,
2,
5458,
12,
18,
13,
17,
7061,
6,
198,
220,
220,
220,
285,
14415,
62,
11925,
796,
651,
62,
600,
17,
7,
7890,
11,
11677,
8,
198,
220,
220,
220,
11677,
15853,
362,
198,
220,
220,
220,
284,
62,
14681,
796,
357,
3149,
72,
62,
11925,
1343,
767,
8,
3373,
807,
198,
220,
220,
220,
285,
14415,
796,
657,
198,
220,
220,
220,
1312,
796,
532,
19,
198,
220,
220,
220,
329,
1312,
287,
2837,
7,
15,
11,
284,
62,
14681,
532,
513,
11,
604,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
285,
14415,
9959,
28,
3933,
198,
220,
220,
220,
220,
220,
220,
220,
285,
14415,
15853,
651,
62,
600,
19,
7,
7890,
11,
11677,
1343,
1312,
8,
198,
220,
220,
220,
329,
474,
287,
2837,
7,
72,
1343,
604,
11,
284,
62,
14681,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
285,
14415,
9959,
28,
807,
198,
220,
220,
220,
220,
220,
220,
220,
285,
14415,
15853,
1366,
58,
28968,
1343,
474,
60,
198,
220,
220,
220,
1303,
11361,
513,
13,
17,
290,
1568,
5559,
25,
198,
220,
220,
220,
1303,
3149,
72,
796,
493,
13,
6738,
62,
33661,
7,
7890,
58,
28968,
25,
28968,
1343,
284,
62,
14681,
4357,
18022,
2875,
11639,
14261,
11537,
198,
220,
220,
220,
11677,
15853,
284,
62,
14681,
198,
220,
220,
220,
1441,
285,
14415,
11,
11677,
628,
198,
4299,
651,
62,
33095,
62,
7890,
7,
7890,
11,
11677,
11,
18022,
62,
9127,
2599,
198,
220,
220,
220,
705,
7061,
42940,
262,
1813,
1271,
286,
9881,
422,
1366,
379,
11677,
290,
1441,
355,
257,
198,
220,
220,
220,
17910,
12,
12685,
9043,
4731,
2637,
7061,
198,
220,
220,
220,
1994,
62,
7890,
796,
1366,
58,
28968,
25,
28968,
1343,
18022,
62,
9127,
60,
198,
220,
220,
220,
611,
350,
56,
2075,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1994,
62,
7890,
796,
11876,
7,
2539,
62,
7890,
8,
198,
220,
220,
220,
1994,
62,
312,
796,
9874,
292,
979,
72,
13,
33095,
75,
1958,
7,
2539,
62,
7890,
8,
198,
220,
220,
220,
1441,
1994,
62,
312,
13,
45828,
3419,
628,
198,
4299,
651,
62,
2539,
62,
312,
7,
7890,
11,
11677,
2599,
198,
220,
220,
220,
705,
7061,
42940,
3624,
9881,
422,
1366,
379,
11677,
290,
1441,
355,
257,
1467,
12,
26327,
17910,
12,
12685,
9043,
198,
220,
220,
220,
4731,
2637,
7061,
198,
220,
220,
220,
1441,
651,
62,
33095,
62,
7890,
7,
7890,
11,
11677,
11,
807,
8,
628,
198,
4299,
651,
62,
600,
62,
33661,
7,
7890,
2599,
198,
220,
220,
220,
705,
7061,
3855,
262,
1263,
12,
437,
666,
18022,
1296,
286,
281,
18253,
393,
4904,
40,
2637,
7061,
198,
220,
220,
220,
17910,
2100,
796,
705,
4,
55,
6,
4064,
1366,
198,
220,
220,
220,
649,
62,
11925,
796,
357,
11925,
7,
33095,
2100,
8,
1343,
352,
8,
3373,
362,
1635,
362,
198,
220,
220,
220,
17910,
2100,
796,
17910,
2100,
13,
89,
20797,
7,
3605,
62,
11925,
8,
198,
220,
220,
220,
1441,
9874,
292,
979,
72,
13,
403,
33095,
75,
1958,
7,
33095,
2100,
13,
268,
8189,
10786,
292,
979,
72,
6,
4008,
628,
198,
4299,
2353,
62,
7890,
7,
7890,
2599,
198,
220,
220,
220,
705,
7061,
11869,
11629,
540,
286,
13934,
1366,
656,
257,
416,
9288,
1806,
611,
3306,
2637,
7061,
198,
220,
220,
220,
611,
350,
56,
2075,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2878,
13,
8002,
10786,
4,
36077,
6,
4064,
18896,
7,
7890,
828,
1635,
7890,
8,
198,
220,
220,
220,
1441,
1366,
198,
198,
4299,
976,
62,
2539,
7,
2539,
62,
64,
11,
1994,
62,
65,
2599,
198,
220,
220,
220,
705,
7061,
50249,
1653,
2163,
329,
1994,
4522,
393,
25338,
13042,
11,
2263,
656,
198,
220,
220,
220,
1848,
15874,
4129,
2637,
7061,
198,
220,
220,
220,
611,
18896,
7,
2539,
62,
64,
8,
6624,
18896,
7,
2539,
62,
65,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
1994,
62,
64,
6624,
1994,
62,
65,
198,
220,
220,
220,
1288,
361,
18896,
7,
2539,
62,
64,
8,
1279,
18896,
7,
2539,
62,
65,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
1994,
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1441,
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62,
64,
13,
437,
2032,
342,
7,
2539,
62,
65,
8,
198
] | 1.851538 | 3,186 |
import parser_asc as parser_asc
f = open("./entrada.txt", "r")
input = f.read()
instrucciones = parser_asc.parse(input)
print('**************** Consola: ****************')
for element in parser_asc.consola:
print(element)
print('**************** Salida: ****************')
for element in parser_asc.salida:
print(element) | [
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import pygame
import pygame.locals
from random import randrange, random
try:
pygame.init()
print("O modulo pygame foi inicializado com sucesso")
except:
print("O modulo pygame não foi inicializado com sucesso")
altura = 800
largura = 800
relogio = pygame.time.Clock()
fundo = pygame.display.set_mode((altura,largura))
pygame.display.set_caption("Star Field")
sky = []
for i in range(300):
star = Star()
sky.append(star)
true = True
while true:
for event in pygame.event.get():
if event.type == pygame.QUIT:
true = False
break
if event.type == pygame.KEYDOWN:
if event.key == pygame.K_SPACE:
true = False
fundo.fill((0,0,0))
for star in sky:
star.show()
star.move()
pygame.display.update()
relogio.tick(60)
pygame.quit() | [
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9078,
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] | 2.248021 | 379 |
# -*- coding: utf-8 -*-
DEFAULT_TOKEN = b"ThisIsAnInsecurePublicTokenDontUseIt"
DEFAULT_SALT = "Quisque vulputate nibh eros, vehicula sollicitudin neque pellentesque quis"
DEFAULT_LOGIN_PATH = 'ajaxlogin'
DEFAULT_NEW_TOKEN_PATH = 'ajaxtoken'
| [
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] | 2.342857 | 105 |
from util import excute_query_, excute_query
from util import database
import sys
if __name__ == '__main__':
testfile = sys.argv[1]
main(testfile)
| [
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220,
220,
198
] | 2.164706 | 85 |
"""Hazelcast Core objects and constants."""
import json
CLIENT_TYPE = "PYH"
SERIALIZATION_VERSION = 1
class MemberInfo(object):
"""
Represents a member in the cluster with its address, uuid, lite member
status, attributes, version, and address map.
"""
__slots__ = ("address", "uuid", "attributes", "lite_member", "version", "address_map")
class Address(object):
"""Represents an address of a member in the cluster."""
class ProtocolType(object):
"""Types of server sockets.
A member typically responds to several types of protocols for
member-to-member, client-member protocol, WAN communication etc. The
default configuration uses a single server socket to listen for all kinds
of protocol types configured, while Advanced Network Config of the server
allows configuration of multiple server sockets.
"""
# We had to put dummy documentations for the constants
# so that they are displayed on the API documentation.
MEMBER = 0
"""Type of member server sockets."""
CLIENT = 1
"""Type of client server sockets."""
WAN = 2
"""Type of WAN server sockets."""
REST = 3
"""Type of REST server sockets."""
MEMCACHE = 4
"""Type of Memcached server sockets."""
class EndpointQualifier(object):
"""Uniquely identifies groups of network connections sharing a common
:class:`ProtocolType` and the same network settings, when Hazelcast server
is configured with Advanced Network Configuration enabled.
In some cases, just the :class:`ProtocolType` is enough (e.g. since there
can be only a single member server socket).
When just the :class:`ProtocolType` is not enough (for example when
configuring outgoing WAN connections to 2 different target clusters),
an :attr:`identifier` is used to uniquely identify the network
configuration.
"""
__slots__ = ("_protocol_type", "_identifier")
@property
def protocol_type(self):
"""ProtocolType: Protocol type of the endpoint."""
return self._protocol_type
@property
def identifier(self):
"""str: Unique identifier for same-protocol-type endpoints."""
return self._identifier
class DistributedObjectEventType(object):
"""Type of the distributed object event."""
CREATED = "CREATED"
"""
DistributedObject is created.
"""
DESTROYED = "DESTROYED"
"""
DistributedObject is destroyed.
"""
class DistributedObjectEvent(object):
"""Distributed Object Event"""
class SimpleEntryView(object):
"""EntryView represents a readonly view of a map entry."""
class HazelcastJsonValue(object):
"""HazelcastJsonValue is a wrapper for JSON formatted strings.
It is preferred to store HazelcastJsonValue instead of Strings for JSON formatted strings.
Users can run predicates and use indexes on the attributes of the underlying
JSON strings.
HazelcastJsonValue is queried using Hazelcast's querying language.
In terms of querying, numbers in JSON strings are treated as either
Long or Double in the Java side. str, bool and None
are treated as String, boolean and null respectively.
HazelcastJsonValue keeps given string as it is. Strings are not
checked for being valid. Ill-formatted JSON strings may cause false
positive or false negative results in queries.
HazelcastJsonValue can also be constructed from JSON serializable objects.
In that case, objects are converted to JSON strings and stored as such.
If an error occurs during the conversion, it is raised directly.
None values are not allowed.
"""
def to_string(self):
"""Returns unaltered string that was used to create this object.
Returns:
str: The original string.
"""
return self._json_string
def loads(self):
"""Deserializes the string that was used to create this object
and returns as Python object.
Returns:
any: The Python object represented by the original string.
"""
return json.loads(self._json_string)
class MemberVersion(object):
"""
Determines the Hazelcast codebase version in terms of major.minor.patch version.
"""
__slots__ = ("major", "minor", "patch")
class MapEntry(object):
"""
Represents the entry of a Map, with key and value fields.
"""
__slots__ = ("_key", "_value")
@property
def key(self):
"""Key of the entry."""
return self._key
@property
def value(self):
"""Value of the entry."""
return self._value
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] | 3.175222 | 1,461 |
from .signate import Signate
__all__ = ['Signate'] | [
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"""Unit test package for otpstore."""
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#!/usr/bin/python
from Solution import Solution
obj = Solution()
#A = [2,3,-2,4]
A = [-2,0,-1]
print(obj.maxProduct(A))
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# -*- coding: utf-8 -*-
# ----------------------------------------------------------------------
# Copyright (c) 2020
#
# See the LICENSE file for details
# see the AUTHORS file for authors
# ----------------------------------------------------------------------
#--------------------
# System wide imports
# -------------------
import sys
import sqlite3
import os.path
import glob
import logging
import csv
import datetime
import math
import hashlib
import time
import re
import collections
# ---------------------
# Third party libraries
# ---------------------
#--------------
# local imports
# -------------
from . import AZOTEA_CSV_DIR, AZOTEA_CFG_DIR
from .camera import CameraImage, CameraCache, MetadataError, ConfigError
from .utils import merge_two_dicts, paging, LogCounter
from .exceptions import MixingCandidates, NoUserInfoError
from .config import load_config_file, merge_options
# ----------------
# Module constants
# ----------------
# values for the 'state' column in table
REGISTERED = 0
STATS_COMPUTED = 1
METADATA_UPDATED = 3
DARK_SUBSTRACTED = 3
# (bit) flags for the 'meta_changes' column in table
NO_CHANGES = 0
CAMERA_CHANGES = 1
OBSERVER_CHANGES = 2
# Values for the 'tyoe' column
LIGHT_FRAME = "LIGHT"
BIAS_FRAME = "BIAS"
DARK_FRAME = "DARK"
UNKNOWN = "UNKNOWN"
N_COUNT = 50
# -----------------------
# Module global variables
# -----------------------
log = logging.getLogger("azotea")
if sys.version_info[0] == 2:
import errno
# =======================
# Module global functions
# =======================
# -----------------
# Utility functions
# -----------------
RE_DARK = re.compile(r'.*DARK.*\..{3}')
def latest_session(connection):
'''Get Last recorded session'''
cursor = connection.cursor()
cursor.execute('''
SELECT MAX(session)
FROM image_t
''')
return cursor.fetchone()[0]
def hash(filepath):
'''Compute a hash from the image'''
BLOCK_SIZE = 1048576 # 1MByte, the size of each read from the file
# md5() was the fastest algorithm I've tried
# but I detected a collision, so I now use blake2b with twice the digest size
file_hash = hashlib.blake2b(digest_size=32)
#file_hash = hashlib.md5()
with open(filepath, 'rb') as f:
block = f.read(BLOCK_SIZE)
while len(block) > 0:
file_hash.update(block)
block = f.read(BLOCK_SIZE)
return file_hash.digest()
# --------------
# Image Register
# --------------
def register_insert_image(connection, row):
'''slow version to find out the exact duplicate'''
cursor = connection.cursor()
cursor.execute(
'''
INSERT OR IGNORE INTO image_t (
name,
hash,
session,
type,
state,
meta_changes
) VALUES (
:name,
:hash,
:session,
:type,
:state,
:changes
)
''', row)
connection.commit()
def register_insert_images(connection, rows):
'''fast version'''
cursor = connection.cursor()
cursor.executemany(
'''
INSERT OR IGNORE INTO image_t (
name,
hash,
session,
type,
state,
meta_changes
) VALUES (
:name,
:hash,
:session,
:type,
:state,
:changes
)
''', rows)
connection.commit()
log.info("Registered %d / %d images in database", cursor.rowcount, len(rows))
def candidates(connection, work_dir, filt, session):
'''candidate list of images to be inserted/removed to/from the database'''
# New Images in the work dir that should be added to database
cursor = connection.cursor()
# This commented query may take long to execute if the database is large
# better we leave it fail in the insertion, where the hash duplication is detected
# cursor.execute(
# '''
# SELECT name, hash
# FROM candidate_t
# WHERE hash NOT IN (SELECT hash FROM image_t)
# '''
# )
# This query will have far less elements to fetch
# This will introduce duplicates which will be rejected by the INSERT or IGNORE
# when inserting new images
cursor.execute("SELECT name, hash FROM candidate_t")
result = cursor.fetchall()
if result:
#names_to_add, = zip(*result)
names_to_add = result
else:
names_to_add = []
# Images no longer in the work dir, they should be deleted from database
row = {'session': session}
cursor.execute(
'''
SELECT name, hash
FROM image_t
WHERE session = :session
AND hash NOT IN (SELECT hash FROM candidate_t)
''', row)
result = cursor.fetchall()
if result:
#names_to_del, = zip(*result)
names_to_del = result
else:
names_to_del = []
return names_to_add, names_to_del
def register_delete_images(connection, rows):
'''delete images'''
cursor = connection.cursor()
cursor.executemany(
'''
DELETE FROM image_t
WHERE hash == :hash
''', rows)
connection.commit()
log.info("Deleted %d / %d images from database", cursor.rowcount, len(rows))
# Tal como esta montado ahora candidates(), es imposible introducir una imagen
# duplicada porque se cumprueba primero que su hash no esta ya en la BD
# Y por tanbo register_low() es innecesario.
# Sin embargo candidates() podría enlentecerse al aumentar el número de imagenes de la BD
# Por lo que al final register_slow() podría ser una opcion
# --------------
# Image Classify
# --------------
# -----------
# Image Stats
# -----------
def stats_unregister(connection, rows):
'''Unregister an image who gave an exception reaing the pixel data'''
cursor = connection.cursor()
cursor.executemany(
'''
DELETE FROM image_t
WHERE hash == :hash
''', rows)
connection.commit()
log.info("Deleted %d / %d images from database", cursor.rowcount, len(rows))
# ---------------
# Metadata Update
# ---------------
# -----------------------------
# Image Apply Dark Substraction
# -----------------------------
# -----------
# Image Export
# -----------
EXPORT_HEADERS = [
'tstamp' ,
'name' ,
'model' ,
'iso' ,
'roi' ,
'dark_roi' ,
'exptime' ,
'aver_signal_R1' ,
'std_signal_R1' ,
'aver_signal_G2' ,
'std_signal_G2' ,
'aver_signal_G3' ,
'std_signal_G3' ,
'aver_signal_B4' ,
'std_signal_B4' ,
'aver_dark_R1' ,
'std_dark_R1' ,
'aver_dark_G2' ,
'std_dark_G2' ,
'aver_dark_G3' ,
'std_dark_G3' ,
'aver_dark_B4' ,
'std_dark_B4' ,
'bias' ,
]
# we are not using the image_v VIEW for the time being
# We display the RAW data without dark and bias substraction
# we are not using the image_v VIEW for the time being
# We display the RAW data without dark and bias substraction
def var2std(item):
'''From Variance to StdDev in several columns'''
index, value = item
# Calculate stddev from variance and round to one decimal place
if index in [13, 15, 17, 19, 21, 23, 25, 27]:
value = round(math.sqrt(value),1)
# Round the aver_signal channels too
elif index in [12, 14, 16, 18, 20, 22, 24, 26]:
value = round(value, 1)
return value
def do_export_work_dir(connection, session, work_dir, options):
'''Export a working directory of image redictions to a single file'''
fieldnames = ["session","observer","organization","location","type"]
fieldnames.extend(EXPORT_HEADERS)
if not session_processed(connection, session):
log.info("No new CSV file generation")
return
for (night,) in night_iterable(connection, session):
# Write a session CSV file
session_csv_file = get_file_path(connection, night, work_dir, options)
with myopen(session_csv_file, 'w') as csvfile:
writer = csv.writer(csvfile, delimiter=';')
writer.writerow(fieldnames)
for row in export_session_iterable(connection, session, night):
row = map(var2std, enumerate(row))
writer.writerow(row)
log.info("Saved data to session CSV file {0}".format(session_csv_file))
def do_export_all(connection, options):
'''Exports all the database to a single file'''
fieldnames = ["session","observer","organization","location","type"]
fieldnames.extend(EXPORT_HEADERS)
with myopen(options.csv_file, 'w') as csvfile:
writer = csv.writer(csvfile, delimiter=';')
writer.writerow(fieldnames)
for row in export_all_iterable(connection):
row = map(var2std, enumerate(row))
writer.writerow(row)
log.info("Saved data to global CSV file {0}".format(options.csv_file))
# ==================================
# Image List subcommands and options
# ==================================
EXIF_HEADERS = [
'Name',
'Session',
'Timestamp',
'Model',
'Exposure',
'ISO',
'Focal',
'f/'
]
GLOBAL_HEADERS = [
'Name',
'Type',
'Session',
'Observer',
'Organiztaion',
'Location',
'ROI',
]
STATE_HEADERS = [
"Name",
"Session",
"Type",
"State",
]
DATA_HEADERS = [
"Name", "ROI", "Bias",
"\u03BC R1", "\u03C3^2 R1",
"\u03BC G2", "\u03C3^2 G2",
"\u03BC G3", "\u03C3^2 G3",
"\u03BC B4", "\u03C3^2 B4",
]
RAW_DATA_HEADERS = [
"Name", "ROI" , "Bias",
"Raw \u03BC R1", "Raw \u03C3^2 R1",
"Raw \u03BC G2", "Raw \u03C3^2 G2",
"Raw \u03BC G3", "Raw \u03C3^2 G3",
"Raw \u03BC B4", "Raw \u03C3^2 B4",
]
DARK_DATA_HEADERS = [
"Name", "ROI" , "Bias",
"Dark \u03BC R1", "Dark \u03C3^2 R1",
"Dark \u03BC G2", "Dark \u03C3^2 G2",
"Dark \u03BC G3", "Dark \u03C3^2 G3",
"Dark \u03BC B4", "Dark \u03C3^2 B4",
]
# --------------
# Image metadata
# --------------
def view_meta_exif_session_iterable(connection, session):
'''session may be None for NULL'''
row = {'session': session}
cursor = connection.cursor()
count = view_session_count(cursor, session)
cursor.execute(
'''
SELECT name, session, tstamp, model, exptime, iso, focal_length, f_number
FROM image_t
WHERE session = :session
ORDER BY name DESC
''', row)
return cursor, count
# ------------
# Image General
# -------------
def view_meta_global_session_iterable(connection, session):
'''session may be None for NULL'''
row = {'session': session}
cursor = connection.cursor()
count = view_session_count(cursor, session)
cursor.execute(
'''
SELECT name, type, session, observer, organization, email, location, roi
FROM image_t
WHERE session = :session
ORDER BY name ASC
''', row)
return cursor, count
# -----------
# Image State
# -----------
# -----------
# Image Data
# -----------
# -------------
# Raw Image Data
# --------------
# --------------
# Dark Image Data
# ---------------
# ----------------
# View Master Dark
# -----------------
MASTER_DARK_HEADERS = [
"Session",
"# Darks",
"ROI",
"Good?",
"\u03BC R1", "\u03C3^2 R1",
"\u03BC G2", "\u03C3^2 G2",
"\u03BC G3", "\u03C3^2 G3",
"\u03BC B4", "\u03C3^2 B4",
]
# ---------
# View Dark
# ----------
# =====================
# Command esntry points
# =====================
# These display various data
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] | 2.305798 | 5,278 |
import aiofiles
from hashlib import md5
from pathlib import Path
from core import mongodb
from decouple import config
from base64 import decodebytes
from core.buchi_exception import BuchiException
from services.pet_finder_service import get_pet_finder_pets
pets_collection = mongodb.buchi.get_collection('pets')
host = config("HOST")
port = config("PORT")
cdn_url = config("CDN_HOST_URL_PREFIX")
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] | 3.150794 | 126 |
"""Console script for unifi_protect_backup."""
import asyncio
import click
from unifi_protect_backup import UnifiProtectBackup, __version__
@click.command()
@click.version_option(__version__)
@click.option('--address', required=True, envvar='UFP_ADDRESS', help='Address of Unifi Protect instance')
@click.option('--port', default=443, envvar='UFP_PORT', help='Port of Unifi Protect instance')
@click.option('--username', required=True, envvar='UFP_USERNAME', help='Username to login to Unifi Protect instance')
@click.option('--password', required=True, envvar='UFP_PASSWORD', help='Password for Unifi Protect user')
@click.option(
'--verify-ssl/--no-verify-ssl',
default=True,
envvar='UFP_SSL_VERIFY',
help="Set if you do not have a valid HTTPS Certificate for your instance",
)
@click.option(
'--rclone-destination',
required=True,
envvar='RCLONE_DESTINATION',
help="`rclone` destination path in the format {rclone remote}:{path on remote}."
" E.g. `gdrive:/backups/unifi_protect`",
)
@click.option(
'--retention',
default='7d',
envvar='RCLONE_RETENTION',
help="How long should event clips be backed up for. Format as per the `--max-age` argument of "
"`rclone` (https://rclone.org/filtering/#max-age-don-t-transfer-any-file-older-than-this)",
)
@click.option(
'--rclone-args',
default='',
envvar='RCLONE_ARGS',
help="Optional extra arguments to pass to `rclone rcat` directly. Common usage for this would "
"be to set a bandwidth limit, for example.",
)
@click.option(
'--ignore-camera',
'ignore_cameras',
multiple=True,
envvar="IGNORE_CAMERAS",
help="IDs of cameras for which events should not be backed up. Use multiple times to ignore "
"multiple IDs. If being set as an environment variable the IDs should be separated by whitespace.",
)
@click.option(
'-v',
'--verbose',
count=True,
help="How verbose the logging output should be."
"""
\n
None: Only log info messages created by `unifi-protect-backup`, and all warnings
-v: Only log info & debug messages created by `unifi-protect-backup`, and all warnings
-vv: Log info & debug messages created by `unifi-protect-backup`, command output, and all warnings
-vvv Log debug messages created by `unifi-protect-backup`, command output, all info messages, and all warnings
-vvvv: Log debug messages created by `unifi-protect-backup` command output, all info messages,
all warnings, and websocket data
-vvvvv: Log websocket data, command output, all debug messages, all info messages and all warnings
""",
)
def main(**kwargs):
"""A Python based tool for backing up Unifi Protect event clips as they occur."""
loop = asyncio.get_event_loop()
event_listener = UnifiProtectBackup(**kwargs)
loop.run_until_complete(event_listener.start())
if __name__ == "__main__":
main() # pragma: no cover
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] | 2.956478 | 988 |
import os
import re
import codecs
from setuptools import setup, find_packages
# Single-source the version from legume/__init__.py
here = os.path.abspath(os.path.dirname(__file__))
#
with open('README.md', 'r') as f:
readme = f.read()
with open('requirements.txt') as f:
requirements = f.read().splitlines()
setup(
name='legume-gme',
version=find_version('legume', '__init__.py'),
description='Differentiable plane-wave and guided-mode expansion for photonic crystals',
long_description=readme,
long_description_content_type="text/markdown",
author='Momchil Minkov',
author_email='[email protected]',
url='https://github.com/fancompute/legume',
packages=find_packages(),
install_requires=requirements,
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
include_package_data=True,
)
| [
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] | 2.747126 | 348 |
import os
from enum import Enum
from typing import List
import attr
from sleap.io.format.filehandle import FileHandle
@attr.s(auto_attribs=True)
class Adaptor(object):
"""
Abstract base class which defines interface for file format adaptors.
"""
@property
def handles(self) -> SleapObjectType:
"""Returns the type of object that can be read/written."""
raise NotImplementedError
@property
@property
@property
def can_read_file(self, file: FileHandle) -> bool:
"""Returns whether this adaptor can read this file."""
raise NotImplementedError
def can_write_filename(self, filename: str) -> bool:
"""Returns whether this adaptor can write format of this filename."""
raise NotImplementedError
def does_read(self) -> bool:
"""Returns whether this adaptor supports reading."""
raise NotImplementedError
def does_write(self) -> bool:
"""Returns whether this adaptor supports writing."""
raise NotImplementedError
def read(self, file: FileHandle) -> object:
"""Reads the file and returns the appropriate deserialized object."""
raise NotImplementedError
def write(self, filename: str, source_object: object):
"""Writes the object to a file."""
raise NotImplementedError
# Methods with default implementation
def does_match_ext(self, filename: str) -> bool:
"""Returns whether this adaptor can write format of this filename."""
# We don't match the ext against the result of os.path.splitext because
# we want to match extensions like ".json.zip".
return filename.endswith(tuple(self.all_exts))
@property
def formatted_ext_options(self):
"""String for Qt file dialog extension options."""
return f"{self.name} ({' '.join(self.all_exts)})"
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7,
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] | 2.874809 | 655 |
import tkinter as tk
import os
import tempfile
root = tk.Tk()
canvas1 = tk.Canvas(root, width=400, height=300)
canvas1.pack()
entry1 = tk.Entry(root)
entry2 = tk.Entry(root)
label2 = tk.Label(root, text='Path')
label1 = tk.Label(root, text='Query')
canvas1.create_window(200, 160, window=entry1)
canvas1.create_window(200, 140, window=label1)
canvas1.create_window(200, 100, window=label2)
canvas1.create_window(200, 120, window=entry2)
button1 = tk.Button(text='Oke', command=letsGO)
canvas1.create_window(200, 180, window=button1)
root.mainloop()
| [
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] | 2.493213 | 221 |
import os
import re
ALLOWED_FLAGS = (0, 16)
DEFAULT_TRAINMODELS_OPTIONS = {
"fofn": None,
"fast5_dir": None,
"positions_file": None,
"motif": None,
"label": None,
}
| [
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] | 2.042553 | 94 |
# Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Unit tests for API calls"""
import unittest
import requests
from mock import patch
class BasicTest(unittest.TestCase):
"""Class to test api calls of Training, Deployment and Prediction"""
def is_not_used(self):
""""Function to remove no-self-use warning"""
pass
@patch('requests.post')
def test_train(self, mock_post):
"""
Test case 1
Tested LinearClassifier model on Census data
"""
self.is_not_used()
params = {"bucket": "gs://cmla",
"train_csv_path": "census_train.csv",
"eval_csv_path": "census_eval.csv",
"task_type": "classification",
"target_var": "income_bracket",
"column_name": "None",
"na_values": "None",
"condition": "None",
"name": "linearclassifier",
"n_classes": 2,
"hidden_units": 64,
"num_layers": 2,
"lin_opt": "ftrl",
"deep_opt": "adam",
"train_steps": 500,
"export_dir": "saved_model/export_dir",
"to_drop": "None"
}
_ = requests.post('http://127.0.0.1:8080/train', data=params,
headers={'Content-Type': 'application/json'})
mock_post.assert_called_with(
"http://127.0.0.1:8080/train", data=params, headers={'Content-Type': 'application/json'})
@patch('requests.post')
def test_deploy(self, mock_post):
"""
Deploy call
Positive test
"""
self.is_not_used()
params = {
"job_id": "C46f52b9f_9019101920_d7583d1f8286",
"model_name": "testing",
"runtime_version": "1.12",
"version_name": "v6_1",
"trained_model_location": "gs://cmla/saved_model/export_dir"
}
_ = requests.post('http://127.0.0.1:8080/deploy', data=params,
headers={'Content-Type': 'application/json'})
mock_post.assert_called_with(
"http://127.0.0.1:8080/deploy", data=params, headers={'Content-Type': 'application/json'})
@patch('requests.post')
def test_predict(self, mock_post):
"""
Testing predict call
Positive
"""
self.is_not_used()
params = {"model_name": "testing",
"instances": [{"capital_gain": 0,
"relationship": "Unmarried",
"gender": "Female",
"marital_status": "Divorced",
"education": "7th-8th",
"fnlwgt": 140359,
"occupation": "Machine-op-inspct",
"capital_loss": 3900,
"workclass": "Private",
"age": 54,
"native_country": "United-States",
"race": "White",
"education_num": 4,
"hours_per_week": 40}],
"version_name": "v6_1"}
_ = requests.post('http://127.0.0.1:8080/predict', data=params,
headers={'Content-Type': 'application/json'})
mock_post.assert_called_with(
"http://127.0.0.1:8080/predict", data=params, headers={'Content-Type': 'application/json'})
| [
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] | 1.912963 | 2,160 |
"""
Tests for Helpers
=================
"""
import pyconfig
from humbledb import _version
from humbledb import Document, Mongo
from humbledb.helpers import auto_increment
from humbledb.errors import DatabaseMismatch, NoConnection
from ..util import DBTest, database_name, eq_, ok_, raises, SkipTest
SIDECAR = 'sidecars'
# The safe= keyword doesn't exist in 3.0
if _version._lt('3.0.0'):
_safe = {'safe': True}
else:
_safe = {}
@raises(DatabaseMismatch)
@raises(NoConnection)
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] | 2.901163 | 172 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
""" Function to search reddit comments and submissions and
return all metadata available and return a dataFrame """
import pandas as pd
import requests
import json
import csv
import time
import datetime
def RedditSearch(query, before='', after='', search_type='hybrid'):
'''
query (string)
after (UTC Timestamp) *** Note that these must be integers ***
DEFAULT: 7 Days before now
before (UTC Timestamp)
DEFAULT: now
search_type (string)
'comment' -> only search comments
'submission' -> only search submissions
'hybrid' -> search both comments and submissions
'''
# Defaults
today = datetime.datetime.utcnow().timestamp()
delta_time = datetime.timedelta(days=7)
if not after or not before:
after = datetime.datetime.now() - delta_time
after = int(after.timestamp())
before = int(today)
print('UTC Before:', before)
print('UTC After:', after)
search_type = search_type.lower()
if search_type not in ['comment', 'submission', 'hybrid']:
print('Unknown search_type, defaulting to hybrid')
search_type = 'hybrid'
subCount = 0 # data counter
commCount = 0 # data counter
subStats = {} # data for storage
commStats = {} #data storage
subList = []
commList = []
# subfunctions
def getPushshiftData_Submission(query, after, before):
'''
query(String) string to search that
after (Timestamp)
before (Timestamp)
'''
url = 'https://api.pushshift.io/reddit/search/submission/?q='+str(query)+\
'&size=1000&after='+str(after)+'&before='+str(before)
# url params well documented at https://github.com/pushshift/api for both comments and submissions
r = requests.get(url)
data = json.loads(r.text)
return data['data']
def getPushshiftData_Comments(query, after, before):
'''
query(String) string to search that
after (Timestamp)
before (Timestamp)
'''
url = 'https://api.pushshift.io/reddit/search/comment/?q='+str(query)+\
')&size=1000&after='+str(after)+'&before='+str(before)
# url params well documented at https://github.com/pushshift/api for both comments and submissions
r = requests.get(url)
data = json.loads(r.text)
return data['data']
try:
# Collect Submissions
# Get initial Submissions that fit query
if search_type != 'comment':
print('Beginning Submission Query')
data = getPushshiftData_Submission(query, after, before)
# Will run until all posts have been gathered i.e. When the length of data variable = 0
# from the 'after' date up until before date
while len(data) > 0:
after_ = int(data[-1]['created_utc'])
for submission in data:
submission['created_utc'] = datetime.datetime.fromtimestamp(submission['created_utc'])
subCount+=1
subList.append(submission)
# Calls getPushshiftData() with the created date of the last submission
print('Oldest Post Date:' + str(data[-1]['created_utc']))
#update after variable to last created date of submission
#data has changed due to the new after variable provided by above code
data = getPushshiftData_Submission(query, after_, before)
print('Submission Query Finished')
# Collect Comments
if search_type != 'submission':
print('Beginning Comment Query')
data = getPushshiftData_Comments(query, after, before)
# Will run until all posts have been gathered i.e. When the length of data variable = 0
# from the 'after' date up until before date
while len(data) > 0:
after_ = int(data[-1]['created_utc'])
for comment in data:
comment['created_utc'] = datetime.datetime.fromtimestamp(comment['created_utc'])
commCount+=1
commList.append(comment)
# Calls getPushshiftData() with the created date of the last submission
print('Oldest Comment Date:' + str((data[-1]['created_utc'])))
#update after variable to last created date of submission
#data has changed due to the new after variable provided by above code
data = getPushshiftData_Comments(query, after_, before)
print('Comment Query Finished')
except:
print('Error while Processing')
# Convert to dfs (sub_id,created,sub,title,text,url,author,score,nsfw,numComms,permalink,flair
print('Building Output')
subDf = pd.DataFrame(subList)
# subDf = subDf.set_index('created_utc')
commDf = pd.DataFrame(commList)
# commDf = commDf.set_index('created_utc')
print('Number of Submissions Collected:', subCount)
print('Number of Comments Collected:', commCount)
return subDf, commDf
submissions, comments = RedditSearch('gummy bears')
submissions.to_csv('submissions.csv')
comments.to_csv('comments.csv')
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13,
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62,
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10786,
15944,
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11537,
201,
198
] | 2.332051 | 2,340 |
from bootstrap3.forms import render_form
from django import template
from ..forms import SearchForm
register = template.Library()
@register.simple_tag()
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] | 3.761905 | 42 |
"""Runs a Blue Green deploy of a Cloud Foundry application using a manifest
"""
from __future__ import print_function
import os
import sys
import json
import cf_api
from cf_api.deploy_manifest import Deploy
from cf_api.deploy_space import Space
from getpass import getpass
print('----------')
# cloud_controller_url = 'https://api.changeme.com'
cloud_controller_url = raw_input('cloud controller url: ').strip()
username = raw_input('username: ').strip()
password = getpass('password: ').strip()
print('----------')
print('Authenticating with UAA...')
cc = cf_api.new_cloud_controller(
cloud_controller_url,
client_id='cf', # the ``cf`` command uses this client and the secret below
client_secret='',
username=username,
password=password,
)
print('Login OK!')
print('----------')
org_name = raw_input('organization name: ').strip()
space_name = raw_input('space name: ').strip()
print('Looking up "{0} / {1}"...'.format(org_name, space_name))
space = Space(cc, org_name=org_name, space_name=space_name, is_debug=True)
print('Found space!')
print('----------')
manifest_path = raw_input('manifest path: ').strip()
manifest_path = os.path.abspath(manifest_path)
space.deploy_blue_green(manifest_path)
print('Deployed {0} successfully!'.format(app_name))
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92,
7675,
0,
4458,
18982,
7,
1324,
62,
3672,
4008,
198
] | 3.081928 | 415 |
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